From c02d428cd094a7cb742e30bc83be9e0a0e7cff60 Mon Sep 17 00:00:00 2001 From: josiah_wong <84cremebrule@gmail.com> Date: Sun, 10 Jan 2021 19:43:36 -0800 Subject: [PATCH 01/16] update docs --- docs/modules/environments.md | 5 ++- docs/modules/objects.md | 71 ++++++++++++++++++++++++------------ 2 files changed, 50 insertions(+), 26 deletions(-) diff --git a/docs/modules/environments.md b/docs/modules/environments.md index a67c6ae418..25bab222eb 100644 --- a/docs/modules/environments.md +++ b/docs/modules/environments.md @@ -107,8 +107,9 @@ print("rollout completed with return {}".format(ret)) - `robot0_proprio-state`, `robot1_proprio-state` : proprioception observations for each robot arm. This includes the arm joint positions (encoded using `sin` and `cos`), arm joint velocities, end effector pose, gripper finger positions, and gripper finger velocities. The shape for this modality is flat (e.g. `(N,)`). - `object-state` : task-specific object observations. For example, the `TwoArmLift` environment provides the pose of the pot, the position of each handle, and the relative position of each robot gripper with respect to each handle. The shape for this modality is flat (e.g. `(N,)`). -- `{camera_name}_image` : image observations for camera with name `camera_name`. The shape for this modality is `(H, W, 3)` where `H` and `W` are the height and width of the image respectively. The image is **flipped** across the height dimension due to issues with `mujoco-py` - so we recommend applying an operation like `im = im[::-1]` to fix this. -- `{camera_name}_depth` : depth image observations for camera with name `camera_name`. The shape for this modality is `(H, W)` where `H` and `W` are the height and width of the image respectively. The image is **flipped** across the height dimension due to issues with `mujoco-py` - so we recommend applying an operation like `im = im[::-1]` to fix this. +- `{camera_name}_image` : image observations for camera with name `camera_name`. The shape for this modality is `(H, W, 3)` where `H` and `W` are the height and width of the image respectively. By default, the returned image convention is mujoco's native `opengl` ("flipped"). This can alternatively be set to `opencv` convention (unflipped) via the `IMAGE_CONVENTION` macro in `macros.py`. +- `{camera_name}_depth` : depth image observations for camera with name `camera_name`. The shape for this modality is `(H, W)` where `H` and `W` are the height and width of the image respectively. By default, the returned image convention is mujoco's native `opengl` ("flipped"). This can alternatively be set to `opencv` convention (unflipped) via the `IMAGE_CONVENTION` macro in `macros.py`. +- `image-state` : (optional) stacked image observations. Note that this is disabled by default, and can be toggled via the `CONCATENATE_IMAGES` macro in `macros.py`. ### Rewards and Termination diff --git a/docs/modules/objects.md b/docs/modules/objects.md index 638472401c..57eeceb2ba 100644 --- a/docs/modules/objects.md +++ b/docs/modules/objects.md @@ -8,16 +8,32 @@ Objects, such as boxes and cans, are essential to building manipulation environm ## The MujocoObject class ```python -class MujocoObject: +class MujocoObject(MujocoModel): def __init__(...): - pass + + ... + + # Attributes that should be filled in within the subclass + self._name = None + self._obj = None + + # Attributes that are auto-filled by _get_object_properties call + self._root_body = None + self._bodies = None + self._joints = None + self._actuators = None + self._sites = None + self._contact_geoms = None + self._visual_geoms = None ``` -`MujocoObject` is the base class of all objects. One must note that it is not a subclass of `MujocoXML`. The XML of an object is generated through the call to `get_collision` and `get_visual`. Both calls take a boolean argument `site`, which adds a site to the center of the object if set. A site is helpful for locating the object in simulation. +`MujocoObject` is the base class of all objects. One must note that it is not a subclass of `MujocoXML`, but does extend from the unifying `MujocoModel` class from which all simulation models (including robots, grippers, etc.) should extend from. All of the attributes shown above prepended with a `_` are intended to be private variables and not accessed by external objects. Instead, any of these properties can be accessed via its public version, without the `_` (e.g.: to access all the object's joints, call `obj.joints` instead of `obj._joints`). This is because all public attributes are automatically post-processed from their private counterparts and have naming prefixes appended to it. + +The XML of an object is generated once during initialization via the `_get_object_subtree` call, after which any external object can extract a reference to this XML via the `get_obj` call. ```python - def get_collision(self, name=None, site=False): + def _get_object_subtree(self): pass - def get_visual(self, name=None, site=False): + def get_obj(self): pass ``` @@ -36,19 +52,21 @@ This allows us to do things like the following. ```python table_top = np.array([0, 1, 0]) bottom_offset = obj.get_bottom_offset() -pos = table_top - bottom_offset # pos + bottom_offset = table_top -obj_xml = obj.get_visual().set("pos", array_to_string(pos)) +pos = table_top - bottom_offset # pos + bottom_offset = table_top +obj_xml = obj.get_obj().set("pos", array_to_string(pos)) # Set the top-level body of this object ``` ## Creating a XMLObject -One can use MuJoCo MJCF XML to generate an object, either as a composition of primitive [geoms](http://mujoco.org/book/modeling.html#geom) or imported from STL files of triangulated [meshes](http://www.mujoco.org/book/modeling.html#mesh). An example is `robosuite.models.objects.xml_objects.BreadObject`. Its [python definition](../../../robosuite/models/objects/xml_objects.py#L41) is short. Note that all `MujocoXMLOBject` classes require both a `name` and `joints` argument, the former which specifies the in-sim name of the object instantiated and the latter which specifies the joint parameters for that object. +One can use MuJoCo MJCF XML to generate an object, either as a composition of primitive [geoms](http://mujoco.org/book/modeling.html#geom) or imported from STL files of triangulated [meshes](http://www.mujoco.org/book/modeling.html#mesh). An example is `robosuite.models.objects.xml_objects.BreadObject`. Its [python definition](../../../robosuite/models/objects/xml_objects.py#L41) is short. Note that all `MujocoXMLObject` classes require both a `fname` and `name` argument, the former which specifies the filepath to the raw XML file and the latter which specifies the in-sim name of the object instantiated. The optional `joints` argument can also specify a custom set of joints to apply to the given object (defaults to "default", which is a single free joint). Generally, the normal use case is to define a single class corresponding to a specific XML file, as shown below: ```python class BreadObject(MujocoXMLObject): - def __init__(self, name=None, joints=None): - super().__init__(xml_path_completion("objects/bread.xml"), name=name, joints=name) + def __init__(self, name): + super().__init__(xml_path_completion("objects/bread.xml"), + name=name, joints=[dict(type="free", damping="0.0005")], + obj_type="all", duplicate_collision_geoms=True) ``` -So all the important definitions are in the [bread.xml](../robosuite/models/assets/objects/bread.xml) file. +In the corresponding XML file, a few key definitions must be present. The top-level, un-named body must contain as immediate children tags (a) the actual object bodie(s) (the top-level **must** be named `object`) and (b) three site tags named `bottom_site`, `top_site`, and `horizontal_radius_site` and whose `pos` values must be specified. The example for the `BreadObject`, [bread.xml](../robosuite/models/assets/objects/bread.xml), is shown below: ```xml @@ -58,12 +76,8 @@ So all the important definitions are in the [bread.xml](../robosuite/models/asse - - - - - - + + @@ -72,15 +86,19 @@ So all the important definitions are in the [bread.xml](../robosuite/models/asse ``` -* `get_collision` of `MujocoXmlObject` would look for the `` tag with name `collision`. -* `get_visual` of `MujocoXmlObject` would look for the `` tag with name `visual`. +Concretely, +* `_get_object_subtree` looks for the object bodie(s) as defined by all nested bodie(s) beginning with the `object`-named body tag. * `bottom_site` should be the bottom of the object, i.e. contact point with the surface it is placed on. * `top_site` should be the top of the object, i.e. contact point if something is placed on it. * `horizontal_radius_site` can be any point on a circle in the x-y plane that does not intersect the object. This allows us to place multiple objects without having them collide into one another. -* These attributes will be parsed by the `MujocoXMLObject` class and conform to the `MujocoObject` interface. ## Creating a procedurally generated object -Procedurally generated objects have been used in [several](https://arxiv.org/abs/1802.09564) [recent](https://arxiv.org/abs/1806.09266) [works](https://arxiv.org/abs/1709.07857) to train control policies with improved robustness and generalization. Here you can programmatically generate an MJCF XML of an object from scratch using `xml.etree.ElementTree`, and compose an object of multiple geom primitives. The implementation is straightforward and interested readers should refer to `_get_collision` and `get_visual` method of `MujocoGeneratedObject`, defined [here](../../../robosuite/models/objects/generated_objects.py). Additionally, custom texture definitions are supported on a per-geom level, where specific texture images can be mapped to individual geoms. Our showcase example that incorporates all of these aspects is the `HammerObject` class which is composed of multiple texture-enabled geoms and utilized within the `TwoArmHandover` environment; its definition can be found [here](../../../robosuite/models/objects/generated_objects.py#L13). +Procedurally generated objects have been used in [several](https://arxiv.org/abs/1802.09564) [recent](https://arxiv.org/abs/1806.09266) [works](https://arxiv.org/abs/1709.07857) to train control policies with improved robustness and generalization. Here you can programmatically generate an MJCF XML of an object from scratch using `xml.etree.ElementTree`, and compose an object of multiple geom primitives. The base class for this type of object is `MujocoGeneratedObject`. +**robosuite** natively supports all Mujoco primitive objects with procedurally-generated `PrimitiveObject` classes (`BoxObject`, `BallObject`, `CapsuleObject`, and `CylinderObject`). + +Additionally, **robosuite** supports custom, complex objects that can be defined by collections of primitive geoms (the [`CompositeObject`](../../../robosuite/models/objects/generated_objects.py#L257) class) or even other objects (the [`CompositeBodyObject`](../../../robosuite/models/objects/generated_objects.py#L10) class). The APIs for each of these classes have been standardized for ease of usage, and interested readers should consult the docstrings for each of these classes, as well as provided examples of each class ([`HammerObject`](../../../robosuite/models/objects/composite/hammer.py), [`HingedBoxObject`](../../../robosuite/models/objects/composite_body/hinged_box.py)). + +It should also be noted that all of the above classes extending from the `MujocoGenereatedObject` class automatically supports custom texture definitions on a per-geom level, where specific texture images can be mapped to individual geoms. The above `HammerObject` showcases an example applying custom textures to different geoms of the resulting object. ## Placing Objects @@ -88,17 +106,22 @@ Object locations are initialized on every environment reset using instances of t ```python self.placement_initializer = UniformRandomSampler( + name="ObjectSampler", + mujoco_objects=self.cube, x_range=[-0.03, 0.03], y_range=[-0.03, 0.03], - ensure_object_boundary_in_range=False, + rotation_axis='z', rotation=None, + ensure_object_boundary_in_range=False, + ensure_valid_placement=True, + reference_pos=self.table_offset, z_offset=0.01, ) ``` -This will sample the cube location uniformly at random in a box of size `0.03` with random z-rotation, and with an offset of `0.01` above the table surface. +This will sample the `self.cube`'s object location uniformly at random in a box of size `0.03` (`x_range`, `y_range`) with random (`rotation`) z-rotation (`rotation_axis`), and with an offset of `0.01` (`z_offset`) above the table surface location (`reference_pos`). The sampler will also make sure that the entire object boundary falls within the sampling box size (`ensure_object_boundary_in_range`) and does not collide with any placed objects (`ensure_valid_placement`). -Another common sampler is the `SequentialCompositeSampler`, which is useful for composing multiple uniform random placement samplers together. As an example, consider the following code snippet from the `__init__` method of the `NutAssembly` environment class. +Another common sampler is the `SequentialCompositeSampler`, which is useful for composing multiple arbitrary placement samplers together. As an example, consider the following code snippet from the `__init__` method of the `NutAssembly` environment class. ```python self.placement_initializer = SequentialCompositeSampler() From 09908eae2b8ae42dde74a046aa624f0542d835d7 Mon Sep 17 00:00:00 2001 From: josiah_wong <84cremebrule@gmail.com> Date: Mon, 11 Jan 2021 14:54:40 -0800 Subject: [PATCH 02/16] more changes to docs --- docs/modeling/arena.rst | 6 +- docs/modeling/gripper_model.rst | 14 +-- docs/modeling/manipulator_model.rst | 16 +++ docs/modeling/mount_model.rst | 15 +++ docs/modeling/mujoco_model.rst | 38 +++++++ docs/modeling/object_model.rst | 34 ++++--- docs/modeling/robot_model.rst | 23 ++--- docs/modeling/task.rst | 13 ++- docs/modules/environments.md | 33 +++--- docs/modules/objects.md | 52 ++++++---- docs/modules/overview.md | 2 +- robosuite/models/objects/objects.py | 152 ++++++++++++++-------------- 12 files changed, 236 insertions(+), 162 deletions(-) create mode 100644 docs/modeling/manipulator_model.rst create mode 100644 docs/modeling/mount_model.rst create mode 100644 docs/modeling/mujoco_model.rst diff --git a/docs/modeling/arena.rst b/docs/modeling/arena.rst index aa9558d77b..e0c890a215 100644 --- a/docs/modeling/arena.rst +++ b/docs/modeling/arena.rst @@ -1,6 +1,10 @@ Arena ===== +The `Arena` class serves as a base model for building the simulation environment. +By default, this includes a ground plane and visual walls, and child classes extend this +to additionally include other objects, e.g.: a table or bins. + Base Arena ---------- @@ -8,4 +12,4 @@ Base Arena .. automethod:: __init__ .. automethod:: set_origin - .. automethod:: add_pos_indicator + .. automethod:: set_camera diff --git a/docs/modeling/gripper_model.rst b/docs/modeling/gripper_model.rst index 34ddb590d8..e5bde3daeb 100644 --- a/docs/modeling/gripper_model.rst +++ b/docs/modeling/gripper_model.rst @@ -1,23 +1,17 @@ Gripper Model ============= -The `GripperModel` class serves as a direct intermediary class that reads in information from a corresponding gripper XML file and also contains relevant hard-coded information from that XML. In conjunction with the `RobotModel` class, this serves as the core modeling component of the higher-level Robot class used in simulation. +The `GripperModel` class serves as a direct intermediary class that reads in information from a corresponding gripper XML file and also contains relevant hard-coded information from that XML. In conjunction with the `ManipulatorModel` class, this serves as the core modeling component of the higher-level `Manipulator` class used in simulation. Base Gripper Model ------------------ .. autoclass:: robosuite.models.grippers.gripper_model.GripperModel - .. automethod:: hide_visualization .. automethod:: format_action - .. autoproperty:: naming_prefix - .. autoproperty:: visualization_sites - .. autoproperty:: sensors + .. autoproperty:: speed .. autoproperty:: dof .. autoproperty:: init_qpos - .. autoproperty:: speed - .. autoproperty:: _joints - .. autoproperty:: _actuators - .. autoproperty:: _contact_geoms - .. autoproperty:: _visualization_geoms + .. autoproperty:: _important_sites .. autoproperty:: _important_geoms + .. autoproperty:: _important_sensors diff --git a/docs/modeling/manipulator_model.rst b/docs/modeling/manipulator_model.rst new file mode 100644 index 0000000000..7fbc28e343 --- /dev/null +++ b/docs/modeling/manipulator_model.rst @@ -0,0 +1,16 @@ +Manipulator Model +================= + +The `ManipulatorModel` class extends from the base `RobotModel` class, and represents an armed, mounted robot with an optional gripper attached to its end effector. In conjunction with the corresponding `GripperModel` class and `MountModel` class, this serves as the core modeling component of the higher-level `Manipulator` class used in simulation. + +Base Manipulator Model +---------------------- + +.. autoclass:: robosuite.models.robots.manipulators.manipulator_model.ManipulatorModel + + .. automethod:: add_gripper + .. autoproperty:: default_gripper + .. autoproperty:: arm_type + .. autoproperty:: base_xpos_offset + .. autoproperty:: _important_sites + .. autoproperty:: _eef_name \ No newline at end of file diff --git a/docs/modeling/mount_model.rst b/docs/modeling/mount_model.rst new file mode 100644 index 0000000000..9379246c41 --- /dev/null +++ b/docs/modeling/mount_model.rst @@ -0,0 +1,15 @@ +Mount Model +=========== + +The `MountModel` class serves as a direct intermediary class that reads in information from a corresponding mount XML file and also contains relevant hard-coded information from that XML. In conjunction with the `RobotModel` class, this serves as the core modeling component of the higher-level Robot class used in simulation. + +Base Mount Model +---------------- + +.. autoclass:: robosuite.models.mounts.mount_model.MountModel + + .. autoproperty:: top_offset + .. autoproperty:: horizontal_radius + .. autoproperty:: _important_sites + .. autoproperty:: _important_geoms + .. autoproperty:: _important_sensors diff --git a/docs/modeling/mujoco_model.rst b/docs/modeling/mujoco_model.rst new file mode 100644 index 0000000000..aceb627799 --- /dev/null +++ b/docs/modeling/mujoco_model.rst @@ -0,0 +1,38 @@ +Mujoco Model +============ + +The `MujocoModel` class is the foundational class from which all other model classes extend from in robosuite. This class represents a standardized API for all models used in simulation and is the core modeling component that other model classes build upon. The `MujocoXMLModel` is an extension of this class that represents models based on an XML file. + +Base Mujoco Model +----------------- + +.. autoclass:: robosuite.models.base.MujocoModel + + .. automethod:: correct_naming + .. automethod:: set_site_visibility + .. automethod:: exclude_from_prefixing + .. autoproperty:: name + .. autoproperty:: naming_prefix + .. autoproperty:: root_body + .. autoproperty:: bodies + .. autoproperty:: joints + .. autoproperty:: actuators + .. autoproperty:: sites + .. autoproperty:: sensors + .. autoproperty:: contact_geoms + .. autoproperty:: visual_geoms + .. autoproperty:: important_geoms + .. autoproperty:: important_sites + .. autoproperty:: important_sensors + .. autoproperty:: bottom_offset + .. autoproperty:: top_offset + .. autoproperty:: horizontal_radius + + +XML Mujoco Model +---------------- + +.. autoclass:: robosuite.models.base.MujocoXMLModel + + .. autoproperty:: base_offset + .. autoproperty:: contact_geom_rgba \ No newline at end of file diff --git a/docs/modeling/object_model.rst b/docs/modeling/object_model.rst index a19b0888a4..ef6fdb4a25 100644 --- a/docs/modeling/object_model.rst +++ b/docs/modeling/object_model.rst @@ -1,24 +1,29 @@ Object Model ============ -The MujocoObject class serves as a catch-all base class that is used to capture individual simulation objects to -instantiate within a given simulation. This is done in one of two ways via extended classes -- the MujocoXMLObject -reads in information from a corresponding object XML file, whereas the MujocoGeneratedObject proecedurally generates a -custom object using a suite of utility mj modeling functions. In conjunction with the RobotModel, GripperModel, and -Arena classes, these classes serve as the basis for forming the higher level ManipulationTask class which is used to -ultimately generate the MjSim simulation object. +The `MujocoObject` class serves as a catch-all base class that is used to capture individual simulation objects to +instantiate within a given simulation. This is done in one of two ways via extended classes -- the `MujocoXMLObject` +reads in information from a corresponding object XML file, whereas the `MujocoGeneratedObject` proecedurally generates a +custom object using a suite of utility mj modeling functions. In conjunction with the `RobotModel`, and +`Arena` classes, these classes serve as the basis for forming the higher level `Task` class which is used to +ultimately generate the `MjSim` simulation object. Base Object Model ----------------- .. autoclass:: robosuite.models.objects.objects.MujocoObject - .. automethod:: get_bottom_offset - .. automethod:: get_top_offset - .. automethod:: get_horizontal_radius - .. automethod:: get_collision - .. automethod:: get_visual - .. automethod:: get_site_attrib_template + .. automethod:: __init__ + .. automethod:: merge_assets + .. automethod:: get_obj + .. automethod:: exclude_from_prefixing + .. automethod:: _get_object_subtree + .. automethod:: _get_object_properties + .. autoproperty:: important_geoms + .. autoproperty:: important_sites + .. autoproperty:: important_sensors + .. autoproperty:: get_site_attrib_template + .. autoproperty:: get_joint_attrib_template XML Object Model @@ -26,12 +31,17 @@ XML Object Model .. autoclass:: robosuite.models.objects.objects.MujocoXMLObject + .. automethod:: __init__ + .. automethod:: _duplicate_visual_from_collision + .. automethod:: _get_geoms + Generated Object Model ---------------------- .. autoclass:: robosuite.models.objects.objects.MujocoGeneratedObject + .. automethod:: __init__ .. automethod:: sanity_check .. automethod:: get_collision_attrib_template .. automethod:: get_visual_attrib_template diff --git a/docs/modeling/robot_model.rst b/docs/modeling/robot_model.rst index 4ab437c082..e96efa810a 100644 --- a/docs/modeling/robot_model.rst +++ b/docs/modeling/robot_model.rst @@ -1,31 +1,24 @@ Robot Model =========== -The RobotModel class serves as a direct intermediary class that reads in information from a corresponding robot XML -file and also contains relevant hard-coded information from that XML. In conjunction with the GripperModel class, this -serves as the core modeling component of the higher-level Robot class used in simulation. +The `RobotModel` class serves as a direct intermediary class that reads in information from a corresponding robot XML +file and also contains relevant hard-coded information from that XML. This represents an arbitrary robot optionally equipped with a mount via the `MountModel` class and is the core modeling component of the higher-level `Robot` class used in simulation. Base Robot Model ---------------- .. autoclass:: robosuite.models.robots.robot_model.RobotModel - .. automethod:: add_gripper .. automethod:: set_base_xpos .. automethod:: set_base_ori .. automethod:: set_joint_attribute - .. automethod:: correct_naming - .. autoproperty:: naming_prefix - .. autoproperty:: joints - .. autoproperty:: eef_name - .. autoproperty:: robot_base - .. autoproperty:: actuators - .. autoproperty:: contact_geoms + .. automethod:: add_mount .. autoproperty:: dof - .. autoproperty:: gripper + .. autoproperty:: default_mount .. autoproperty:: default_controller_config .. autoproperty:: init_qpos .. autoproperty:: base_xpos_offset - .. autoproperty:: arm_type - .. autoproperty:: _root - .. autoproperty:: _links \ No newline at end of file + .. autoproperty:: _horizontal_radius + .. autoproperty:: _important_sites + .. autoproperty:: _important_geoms + .. autoproperty:: _important_sensors \ No newline at end of file diff --git a/docs/modeling/task.rst b/docs/modeling/task.rst index 9108d344ba..3bb1adc294 100644 --- a/docs/modeling/task.rst +++ b/docs/modeling/task.rst @@ -1,16 +1,15 @@ Task ===== -The Task class is responsible for integrating a given Arena, RobotModel, and set of MujocoObjects into a single element -tree that is then parsed and converted into an MjSim object. It is also responsible for placing the objects within -the simulation. +The `Task` class is responsible for integrating a given `Arena`, `RobotModel`, and set of `MujocoObjects` into a single element +tree that is then parsed and converted into an `MjSim` object. -Manipulation Task +Base Task ----------------- -.. autoclass:: robosuite.models.tasks.ManipulationTask +.. autoclass:: robosuite.models.tasks.task.Task + .. automethod:: __init__ .. automethod:: merge_robot .. automethod:: merge_arena - .. automethod:: merge_objects - .. automethod:: place_objects + .. automethod:: merge_objects \ No newline at end of file diff --git a/docs/modules/environments.md b/docs/modules/environments.md index 25bab222eb..e67cfe5472 100644 --- a/docs/modules/environments.md +++ b/docs/modules/environments.md @@ -1,6 +1,6 @@ # Environments -Environments are the main **robosuite** API objects that external code will interact with. Each environment corresponds to a robot manipulation task and provides a standard interface for an agent to interact with the environment. +Environments are the main **robosuite** API objects that external code will interact with. Each environment corresponds to a robot manipulation task and provides a standard interface for an agent to interact with the environment. While **robosuite** can support environments from different robotic domains, the current release focuses is on manipulation environments. Next, we will describe how to create an environment, how to interact with an environment, and how each environment creates a simulated task in the MuJoCo physics engine. We will use the `TwoArmLift` environment as a running example for each section. @@ -74,7 +74,8 @@ We provide a few additional details on a few keyword arguments below to highligh - `robots` : this argument can be used to easily instantiate tasks with different robot arms. For example, we could change the task to use two "Jaco" robots by passing `robots=["Jaco", "Jaco"]`. Once the environment is initialized, these robots (as captured by the [Robot](robots) class) can be accessed via the `robots` array attribute within the environment, i.e.: `env.robots[i]` for the `ith` robot arm in the environment. - `gripper_types` : this argument can be used to easily swap out different grippers for each robot arm. For example, suppose we want to swap the default grippers for the arms in the example above. We could just pass `gripper_types=["PandaGripper", "RethinkGripper"]` to achieve this. Note that a single type can also be used to automatically broadcast the same gripper type across all arms. - `controller_configs` : this argument can be used to easily replace the action space for each robot arm. For example, if we would like to control the arm using joint velocities instead of OSC, we could use `load_controller_config(default_controller="JOINT_VELOCITY")` in the example above. Similar to `gripper_types` this value can either be per-arm specific or a single configuration to broadcast to all robot arms. -- `env_configuration` : this argument is used for two-arm tasks to easily configure how the robots are oriented with respect to one another. For example, in the `TwoArmLift` environment, we could pass `env_configuration="single-arm-parallel"` instead so that the robot arms are located next to each other, instead of opposite each other +- `env_configuration` : this argument is mainly used for two-arm tasks to easily configure how the robots are oriented with respect to one another. For example, in the `TwoArmLift` environment, we could pass `env_configuration="single-arm-parallel"` instead so that the robot arms are located next to each other, instead of opposite each other +- `placement_initializer` : this argument is optional, but can be used to specify a custom `ObjectPositionSampler` to override the default start state distribution for Mujoco objects. Samplers are responsible for sampling a set of valid, non-colliding placements for all of the objects in the scene at the start of each episode (e.g. when `env.reset()` is called). ## Interacting with an Environment @@ -103,7 +104,7 @@ print("rollout completed with return {}".format(ret)) ### Observations -**robosuite** observations are dictionaries that include key-value pairs per modality. This makes it easy for agents to work with modalities of different shapes (for example, flat proprioception observations, and pixel observations). Below, we list commonly used observation keys. +**robosuite** observations are dictionaries that include key-value pairs per modality. This makes it easy for agents to work with modalities of different shapes (for example, flat proprioception observations, and pixel observations). Note that any observation entry ending with `*-state` represents a concatenation of all individual observations that belong to `*` modality. Below, we list commonly used observation keys. - `robot0_proprio-state`, `robot1_proprio-state` : proprioception observations for each robot arm. This includes the arm joint positions (encoded using `sin` and `cos`), arm joint velocities, end effector pose, gripper finger positions, and gripper finger velocities. The shape for this modality is flat (e.g. `(N,)`). - `object-state` : task-specific object observations. For example, the `TwoArmLift` environment provides the pose of the pot, the position of each handle, and the relative position of each robot gripper with respect to each handle. The shape for this modality is flat (e.g. `(N,)`). @@ -149,26 +150,24 @@ Otherwise, we'll only provide partial rewards if we're using reward shaping, and ```python elif self.reward_shaping: - # lifting reward (smooth value between [0, 2]) + # lifting reward (smooth value between [0, 1.5]) pot_height = get_pot_height() - r_lift = min(max(pot_height - 0.05, 0), 0.2) + r_lift = min(max(pot_height - 0.05, 0), 0.15) reward += 10. * direction_coef * r_lift - # reaching / grasping reward (smooth value between [0, 1]) - left_hand_handle_contact = is_left_contact() - right_hand_handle_contact = is_right_contact() + # reaching reward (smooth value between [0, 1]) left_hand_handle_distance = get_left_distance() right_hand_handle_distance = get_right_distance() + reward += 0.5 * (1 - np.tanh(10.0 * left_hand_handle_distance)) + reward += 0.5 * (1 - np.tanh(10.0 * right_hand_handle_distance)) - # Half of reward for each hand converging to the correct handle + # grasping reward (discrete values between [0, 0.5]) + left_hand_handle_contact = is_left_contact() + right_hand_handle_contact = is_right_contact() if left_hand_handle_contact: - reward += 0.5 - else: - reward += 0.5 * (1 - np.tanh(10.0 * left_hand_handle_distance)) + reward += 0.25 if right_hand_handle_contact: reward += 0.5 - else: - reward += 0.5 * (1 - np.tanh(10.0 * right_hand_handle_distance)) ``` Lastly, we need to normalize our reward and then re-scale its value to `reward_scale` if it is specified before finally returning the calculated reward. @@ -183,13 +182,11 @@ Lastly, we need to normalize our reward and then re-scale its value to `reward_s Every environment owns its own `MJCF` model that sets up the MuJoCo physics simulation by loading the robots, the workspace, and the objects into the simulator appropriately. This MuJoCo simulation model is programmatically instantiated in the `_load_model` function of each environment, by creating an instance of the `Task` class. -Each `Task` class instance owns an `Arena` model, a list of `Robot` model instances, and a list of `Object` model instances. These are **robosuite** classes that introduce a useful abstraction in order to make designing scenes in MuJoCo easy. Every `Arena` is based off of an xml that defines the workspace (for example, table or bins) and camera locations. Every `Robot` is a MuJoCo model of each type of robot arm (e.g. Sawyer, Panda, etc.). Every `Object` model corresponds to a physical object loaded into the simulation (e.g. cube, pot with handles, etc.). - -Each `Task` class instance also takes a `placement_initializer` as input. The `placement_initializer` determines the start state distribution for the environment by sampling a set of valid, non-colliding placements for all of the objects in the scene at the start of each episode (e.g. when `env.reset()` is called). +Each `Task` class instance owns an `Arena` model, a list of `RobotModel` instances, and a list of `ObjectModel` instances. These are **robosuite** classes that introduce a useful abstraction in order to make designing scenes in MuJoCo easy. Every `Arena` is based off of an xml that defines the workspace (for example, table or bins) and camera locations. Every `RobotModel` is a MuJoCo model of representing an arbitrary robot (for `ManipulationModel`s, this represent armed robots, e.g. Sawyer, Panda, etc.). Every `ObjectModel` corresponds to a physical object loaded into the simulation (e.g. cube, pot with handles, etc.). ## Task Descriptions -We provide a brief description of each environment below. For benchmarking results on these standardized environments, please check out the [Benchmarking](../algorithms/benchmarking) page. +While **robosuite** can support environments from different robotic domains, the current release focuses is on manipulation environments (`ManipulationEnv`), We provide a brief description of each environment below. For benchmarking results on these standardized environments, please check out the [Benchmarking](../algorithms/benchmarking) page. ### Single-Arm Tasks diff --git a/docs/modules/objects.md b/docs/modules/objects.md index 57eeceb2ba..d64edc81ae 100644 --- a/docs/modules/objects.md +++ b/docs/modules/objects.md @@ -124,27 +124,35 @@ This will sample the `self.cube`'s object location uniformly at random in a box Another common sampler is the `SequentialCompositeSampler`, which is useful for composing multiple arbitrary placement samplers together. As an example, consider the following code snippet from the `__init__` method of the `NutAssembly` environment class. ```python -self.placement_initializer = SequentialCompositeSampler() -self.placement_initializer.sample_on_top( - "SquareNut0", - surface_name="table", - x_range=[-0.115, -0.11], - y_range=[0.11, 0.225], - rotation=None, - rotation_axis='z', - z_offset=0.02, - ensure_object_boundary_in_range=False, -) -self.placement_initializer.sample_on_top( - "RoundNut0", - surface_name="table", - x_range=[-0.115, -0.11], - y_range=[-0.225, -0.11], - rotation=None, - rotation_axis='z', - z_offset=0.02, - ensure_object_boundary_in_range=False, -) +# Establish named references to each nut object +nut_names = ("SquareNut", "RoundNut") + +# Initialize the top-level sampler +self.placement_initializer = SequentialCompositeSampler(name="ObjectSampler") + +# Create individual samplers per nut +for nut_name, default_y_range in zip(nut_names, ([0.11, 0.225], [-0.225, -0.11])): + self.placement_initializer.append_sampler( + sampler=UniformRandomSampler( + name=f"{nut_name}Sampler", + x_range=[-0.115, -0.11], + y_range=default_y_range, + rotation=None, + rotation_axis='z', + ensure_object_boundary_in_range=False, + ensure_valid_placement=True, + reference_pos=self.table_offset, + z_offset=0.02, + ) + ) + +# No objects have been assigned to any samplers yet, so we do that now +for i, (nut_cls, nut_name) in enumerate(zip( + (SquareNutObject, RoundNutObject), + nut_names, +)): + nut = nut_cls(name=nut_name) + self.placement_initializer.add_objects_to_sampler(sampler_name=f"{nut_name}Sampler", mujoco_objects=nut) ``` -The code snippet above results in two `UniformRandomSampler` instances being used to place the nuts onto the table surface - one for each type of nut. Notice this also allows the nuts to be initialized in separate regions of the table. The `SequentialCompositeSampler` makes it easy to compose multiple placement initializers together in order to specify how different objects should be initialized in an environment. \ No newline at end of file +The code snippet above results in two `UniformRandomSampler` instances being used to place the nuts onto the table surface - one for each type of nut. Notice this also allows the nuts to be initialized in separate regions of the table, and with arbitrary sampling settings. The `SequentialCompositeSampler` makes it easy to compose multiple placement initializers together and assign objects to each sub-sampler in a modular way. \ No newline at end of file diff --git a/docs/modules/overview.md b/docs/modules/overview.md index 88d726859b..9f69bdc5cd 100644 --- a/docs/modules/overview.md +++ b/docs/modules/overview.md @@ -4,7 +4,7 @@ Our framework offers two main categories of APIs: 1) **Modeling APIs** for defining simulation environments in a modular and programmatic fashion, and 2) **Simulation APIs** for interfacing with external inputs such as from a **Policy** or an **I/O Device**. A **Simulation Model** specified by the Modeling APIs is instantiated by the **MuJoCo Engine** to create a simulation runtime, called **Environment**. The Environment generates observations through the **Sensors**, such as cameras and proprioception, and receives action commands from policies or devices through the **Controllers** of the **Robots**. The diagram above illustrates the key components in our framework and their relationships. -A simulation model is defined by a [Task](../modeling/task) object, which encapsulates three essential constituents of robotic simulation: [Robot Model](../modeling/robot_model)s, [Object Model](../modeling/object_model)s, and [Arena](../modeling/arena). A task may contain one or more robots, zero to many objects, and a single arena. The Robot Model loads models of robots and their corresponding [Gripper Model](../modeling/gripper_model)s from XML files. The Object Model can be either loaded from 3D object assets or procedurally generated with programmatic APIs. The Arena defines the workspace of the robot, including the environment fixtures, such as a tabletop, and their placements. The task class combines these constituents into a single XML object in MuJoCo's [MJCF modeling language](http://www.mujoco.org/book/XMLreference.html). This MJCF object is passed to the MuJoCo engine through the [mujoco-py](https://openai.github.io/mujoco-py/build/html/index.html) library to instantiate the [MjSim](https://openai.github.io/mujoco-py/build/html/reference.html#mjsim-basic-simulation) object for simulation runtime. +A simulation model is defined by a [Task](../modeling/task) object, which encapsulates three essential constituents of robotic simulation: [Robot Model](../modeling/robot_model)s, [Object Model](../modeling/object_model)s, and [Arena](../modeling/arena). A task may contain one or more robots, zero to many objects, and a single arena. The Robot Model loads models of robots and optionally other models as well; for example, the [Manipulator](../) robot model class also loads a corresponding [Gripper Model](../modeling/gripper_model)s from XML files. The Object Model can be either loaded from 3D object assets or procedurally generated with programmatic APIs. The Arena defines the workspace of the robot, including the environment fixtures, such as a tabletop, and their placements. The task class combines these constituents into a single XML object in MuJoCo's [MJCF modeling language](http://www.mujoco.org/book/XMLreference.html). This MJCF object is passed to the MuJoCo engine through the [mujoco-py](https://openai.github.io/mujoco-py/build/html/index.html) library to instantiate the [MjSim](https://openai.github.io/mujoco-py/build/html/reference.html#mjsim-basic-simulation) object for simulation runtime. The [Environment](environments) object provides [OpenAI Gym](https://gym.openai.com/)-style APIs for external inputs to interface with the simulation. External inputs correspond to the action commands used to control the [Robots](robots) and their grippers, where the action spaces are specific to the [Controllers](controllers) used by the robots. For instance, for joint-space controllers of a robot arm, the action space corresponds to the number of joints of the robot, and for operational space controllers, the action space corresponds to 3D Cartesian movement or 6D pose of the end-effector. These action commands can either be automatically generated by an algorithm (such as a deep neural network policy) or come from [I/O devices](devices) for human teleoperation (such as the keyboard). The controllers of the robots are responsible for interpreting these action commands and transforming them into the low-level torques passing to the underlying physics engine, which performs internal computations to determine the next state of the simulation. [Sensors](./sensors) retrieve information from the MjSim object and generate observations as the physical signals that the robots receive as response to their actions. Our framework supports multimodal sensing modalities, such as RGB-D cameras, force-torque measurements, and proprioceptive data. In addition to these sensory data, environments also provide additional information about the task progress and success conditions, including reward functions (for reinforcement learning) and other meta-data. For more information, please check out the descriptions of the individual components in this section. diff --git a/robosuite/models/objects/objects.py b/robosuite/models/objects/objects.py index 030d5ab64a..d95d58cb47 100644 --- a/robosuite/models/objects/objects.py +++ b/robosuite/models/objects/objects.py @@ -73,6 +73,76 @@ def merge_assets(self, other): attribs={"name": asset.get("name")}, return_first=True) is None: self.asset.append(asset) + def get_obj(self): + """ + Returns the generated / extracted object, in XML ElementTree form. + + Returns: + ET.Element: Object in XML form. + """ + assert self._obj is not None, "Object XML tree has not been generated yet!" + return self._obj + + def exclude_from_prefixing(self, inp): + """ + A function that should take in either an ET.Element or its attribute (str) and return either True or False, + determining whether the corresponding name / str to @inp should have naming_prefix added to it. + Must be defined by subclass. + + Args: + inp (ET.Element or str): Element or its attribute to check for prefixing. + + Returns: + bool: True if we should exclude the associated name(s) with @inp from being prefixed with naming_prefix + """ + raise NotImplementedError + + def _get_object_subtree(self): + + """ + Returns a ET.Element + It is a subtree that defines all collision and / or visualization related fields + of this object. + Return should be a copy. + Must be defined by subclass. + + Returns: + ET.Element: body + """ + raise NotImplementedError + + def _get_object_properties(self): + """ + Helper function to extract relevant object properties (bodies, joints, contact/visual geoms, etc...) from this + object's XML tree. Assumes the self._obj attribute has already been filled. + """ + # Parse element tree to get all relevant bodies, joints, actuators, and geom groups + _elements = sort_elements(root=self.get_obj()) + assert len(_elements["root_body"]) == 1, "Invalid number of root bodies found for robot model. Expected 1," \ + "got {}".format(len(_elements["root_body"])) + _elements["root_body"] = _elements["root_body"][0] + _elements["bodies"] = [_elements["root_body"]] + _elements["bodies"] if "bodies" in _elements else \ + [_elements["root_body"]] + self._root_body = _elements["root_body"].get("name") + self._bodies = [e.get("name") for e in _elements.get("bodies", [])] + self._joints = [e.get("name") for e in _elements.get("joints", [])] + self._actuators = [e.get("name") for e in _elements.get("actuators", [])] + self._sites = [e.get("name") for e in _elements.get("sites", [])] + self._sensors = [e.get("name") for e in _elements.get("sensors", [])] + self._contact_geoms = [e.get("name") for e in _elements.get("contact_geoms", [])] + self._visual_geoms = [e.get("name") for e in _elements.get("visual_geoms", [])] + + # Add default materials if we're using domain randomization + if macros.USING_INSTANCE_RANDOMIZATION: + tex_element, mat_element, _, used = add_material(root=self.get_obj(), naming_prefix=self.naming_prefix) + # Only add the material / texture if they were actually used + if used: + self.asset.append(tex_element) + self.asset.append(mat_element) + + # Add prefix to all elements + add_prefix(root=self.get_obj(), prefix=self.naming_prefix, exclude=self.exclude_from_prefixing) + @property def name(self): return self._name @@ -139,16 +209,6 @@ def important_sensors(self): """ return {} - def get_obj(self): - """ - Returns the generated / extracted object, in XML ElementTree form. - - Returns: - ET.Element: Object in XML form. - """ - assert self._obj is not None, "Object XML tree has not been generated yet!" - return self._obj - @property def bottom_offset(self): """ @@ -186,66 +246,6 @@ def horizontal_radius(self): """ raise NotImplementedError - def exclude_from_prefixing(self, inp): - """ - A function that should take in either an ET.Element or its attribute (str) and return either True or False, - determining whether the corresponding name / str to @inp should have naming_prefix added to it. - Must be defined by subclass. - - Args: - inp (ET.Element or str): Element or its attribute to check for prefixing. - - Returns: - bool: True if we should exclude the associated name(s) with @inp from being prefixed with naming_prefix - """ - raise NotImplementedError - - def _get_object_subtree(self): - - """ - Returns a ET.Element - It is a subtree that defines all collision and / or visualization related fields - of this object. - Return should be a copy. - Must be defined by subclass. - - Returns: - ET.Element: body - """ - raise NotImplementedError - - def _get_object_properties(self): - """ - Helper function to extract relevant object properties (bodies, joints, contact/visual geoms, etc...) from this - object's XML tree. Assumes the self._obj attribute has already been filled. - """ - # Parse element tree to get all relevant bodies, joints, actuators, and geom groups - _elements = sort_elements(root=self.get_obj()) - assert len(_elements["root_body"]) == 1, "Invalid number of root bodies found for robot model. Expected 1," \ - "got {}".format(len(_elements["root_body"])) - _elements["root_body"] = _elements["root_body"][0] - _elements["bodies"] = [_elements["root_body"]] + _elements["bodies"] if "bodies" in _elements else \ - [_elements["root_body"]] - self._root_body = _elements["root_body"].get("name") - self._bodies = [e.get("name") for e in _elements.get("bodies", [])] - self._joints = [e.get("name") for e in _elements.get("joints", [])] - self._actuators = [e.get("name") for e in _elements.get("actuators", [])] - self._sites = [e.get("name") for e in _elements.get("sites", [])] - self._sensors = [e.get("name") for e in _elements.get("sensors", [])] - self._contact_geoms = [e.get("name") for e in _elements.get("contact_geoms", [])] - self._visual_geoms = [e.get("name") for e in _elements.get("visual_geoms", [])] - - # Add default materials if we're using domain randomization - if macros.USING_INSTANCE_RANDOMIZATION: - tex_element, mat_element, _, used = add_material(root=self.get_obj(), naming_prefix=self.naming_prefix) - # Only add the material / texture if they were actually used - if used: - self.asset.append(tex_element) - self.asset.append(mat_element) - - # Add prefix to all elements - add_prefix(root=self.get_obj(), prefix=self.naming_prefix, exclude=self.exclude_from_prefixing) - @staticmethod def get_site_attrib_template(): """ @@ -369,6 +369,12 @@ def _should_keep(el): return obj + def exclude_from_prefixing(self, inp): + """ + By default, don't exclude any from being prefixed + """ + return False + def _get_object_properties(self): """ Extends the base class method to also add prefixes to all bodies in this object @@ -376,12 +382,6 @@ def _get_object_properties(self): super()._get_object_properties() add_prefix(root=self.root, prefix=self.naming_prefix, exclude=self.exclude_from_prefixing) - def exclude_from_prefixing(self, inp): - """ - By default, don't exclude any from being prefixed - """ - return False - @staticmethod def _duplicate_visual_from_collision(element): """ From 1508652c78add93abb516e80079a9760a237256f Mon Sep 17 00:00:00 2001 From: josiah_wong <84cremebrule@gmail.com> Date: Sat, 30 Jan 2021 18:21:00 -0800 Subject: [PATCH 03/16] update docs --- docs/modules/overview.md | 2 +- docs/modules/renderers.md | 14 ++++++ docs/modules/robots.md | 13 +++--- docs/modules/sensors.md | 76 +++++++++++++++++++++++++++++---- docs/simulation/environment.rst | 35 +++++++++------ docs/simulation/robot.rst | 46 ++++++++++++++------ 6 files changed, 145 insertions(+), 41 deletions(-) create mode 100644 docs/modules/renderers.md diff --git a/docs/modules/overview.md b/docs/modules/overview.md index 9f69bdc5cd..9f514bccad 100644 --- a/docs/modules/overview.md +++ b/docs/modules/overview.md @@ -6,5 +6,5 @@ Our framework offers two main categories of APIs: 1) **Modeling APIs** for defin A simulation model is defined by a [Task](../modeling/task) object, which encapsulates three essential constituents of robotic simulation: [Robot Model](../modeling/robot_model)s, [Object Model](../modeling/object_model)s, and [Arena](../modeling/arena). A task may contain one or more robots, zero to many objects, and a single arena. The Robot Model loads models of robots and optionally other models as well; for example, the [Manipulator](../) robot model class also loads a corresponding [Gripper Model](../modeling/gripper_model)s from XML files. The Object Model can be either loaded from 3D object assets or procedurally generated with programmatic APIs. The Arena defines the workspace of the robot, including the environment fixtures, such as a tabletop, and their placements. The task class combines these constituents into a single XML object in MuJoCo's [MJCF modeling language](http://www.mujoco.org/book/XMLreference.html). This MJCF object is passed to the MuJoCo engine through the [mujoco-py](https://openai.github.io/mujoco-py/build/html/index.html) library to instantiate the [MjSim](https://openai.github.io/mujoco-py/build/html/reference.html#mjsim-basic-simulation) object for simulation runtime. -The [Environment](environments) object provides [OpenAI Gym](https://gym.openai.com/)-style APIs for external inputs to interface with the simulation. External inputs correspond to the action commands used to control the [Robots](robots) and their grippers, where the action spaces are specific to the [Controllers](controllers) used by the robots. For instance, for joint-space controllers of a robot arm, the action space corresponds to the number of joints of the robot, and for operational space controllers, the action space corresponds to 3D Cartesian movement or 6D pose of the end-effector. These action commands can either be automatically generated by an algorithm (such as a deep neural network policy) or come from [I/O devices](devices) for human teleoperation (such as the keyboard). The controllers of the robots are responsible for interpreting these action commands and transforming them into the low-level torques passing to the underlying physics engine, which performs internal computations to determine the next state of the simulation. [Sensors](./sensors) retrieve information from the MjSim object and generate observations as the physical signals that the robots receive as response to their actions. Our framework supports multimodal sensing modalities, such as RGB-D cameras, force-torque measurements, and proprioceptive data. In addition to these sensory data, environments also provide additional information about the task progress and success conditions, including reward functions (for reinforcement learning) and other meta-data. For more information, please check out the descriptions of the individual components in this section. +The [Environment](environments) object provides [OpenAI Gym](https://gym.openai.com/)-style APIs for external inputs to interface with the simulation. External inputs correspond to the action commands used to control the [Robots](robots) and any actuators it owns (for example, in the case of a manipulator robot the arm joints and gripper), where the kinematic component of the action spaces are specific to the [Controllers](controllers) used by the robots. For instance, for joint-space controllers of a robot manipulator arm, the action space corresponds to the number of joints of the robot, and for operational space controllers, the action space corresponds to 3D Cartesian movement or 6D pose of the end-effector. These action commands can either be automatically generated by an algorithm (such as a deep neural network policy) or come from [I/O devices](devices) for human teleoperation (such as the keyboard). The controllers of the robots are responsible for interpreting these action commands and transforming them into the low-level torques passing to the underlying physics engine, which performs internal computations to determine the next state of the simulation. [Sensors](./sensors) retrieve information from the MjSim object and generate observations as the physical signals that the robots receive as response to their actions. Our framework supports multimodal sensing modalities, such as RGB-D cameras, force-torque measurements, and proprioceptive data, and also provides modular APIs to model realistic sensor dynamics. In addition to these sensory data, environments also provide additional information about the task progress and success conditions, including reward functions (for reinforcement learning) and other meta-data. For more information, please check out the descriptions of the individual components in this section. diff --git a/docs/modules/renderers.md b/docs/modules/renderers.md new file mode 100644 index 0000000000..48e96cc055 --- /dev/null +++ b/docs/modules/renderers.md @@ -0,0 +1,14 @@ +# Renderers + +Renderers are used to visualize the simulation, and can be used either in on-screen mode or headless (off-screen) mode. Renderers are also responsible for generating image-based observations that are returned from a given environment, and compute virtual images of the environment based on the properties defined in the cameras. In **robosuite**, the user can select one of these two renderers: + +##### MjViewer + +This is the default renderer from [mujoco-py](https://openai.github.io/mujoco-py/build/html/reference.html#mjviewer-3d-rendering). Based on [OpenGL](https://www.opengl.org/), our assets and environment definitions have been tuned to look good with this renderer. + + + + +##### PyGame + +[PyGame](https://www.pygame.org/news) is a simple renderer that serves also as an alternative to MjViewer. A limitation of PyGame is that it can only render on-screen, limiting its applicability to train on computing clusters. However, it is useful for visualizing the robots' behaviors in the system runtime where MjViewer is not supported. diff --git a/docs/modules/robots.md b/docs/modules/robots.md index e46a217416..d2803a5e9d 100644 --- a/docs/modules/robots.md +++ b/docs/modules/robots.md @@ -2,13 +2,13 @@ ![robot_overview_diagram](../images/robot_module.png) -**Robots** are a key component in **robosuite**, and serve as the embodiment of a given agent as well as the central interaction point within an environment and key interface to MuJoCo for the robot-related state and control. **robosuite** captures this level of abstraction with the [Robot](../simulation/robot)-based classes, with support for both single-armed and bimanual variations. In turn, the Robot class is centrally defined by a [RobotModel](../modeling/robot_model), [GripperModel(s)](../modeling/gripper_model) (with no gripper being represented by a dummy class), and [Controller(s)](../simulation/controller). +**Robots** are a key component in **robosuite**, and serve as the embodiment of a given agent as well as the central interaction point within an environment and key interface to MuJoCo for the robot-related state and control. **robosuite** captures this level of abstraction with the [Robot](../simulation/robot)-based classes, with support for both single-armed and bimanual variations. In turn, the Robot class is centrally defined by a [RobotModel](../modeling/robot_model), [MountModel](../modeling/mount_model), and [Controller(s)](../simulation/controller). Subclasses of the `RobotModel` class may also include additional models as well; for example, the [ManipulatorModel](../modeling/manipulator_model) class also includes [GripperModel(s)](../modeling/gripper_model) (with no gripper being represented by a dummy class). The high-level features of **robosuite**'s robots are described as follows: -* **Diverse and Realistic Models**: **robosuite** provides models for 8 commercially-available robots (including the bimanual Baxter robot), 7 grippers (including the popular Robotiq 140 / 85 models), and 6 controllers, with model properties either taken directly from the company website or raw spec sheets. +* **Diverse and Realistic Models**: **robosuite** provides models for 8 commercially-available manipulator robots (including the bimanual Baxter robot), 7 grippers (including the popular Robotiq 140 / 85 models), and 6 controllers, with model properties either taken directly from the company website or raw spec sheets. -* **Modularized Support**: Robots are designed to be plug-n-play -- any combinations of robots, grippers, and controllers can be used, assuming the given environment is intended for the desired robot configuration. Because each robot is assigned a unique ID number, multiple instances of identical robots can be instantiated within the simulation without error. +* **Modularized Support**: Robots are designed to be plug-n-play -- any combinations of robots, models, and controllers can be used, assuming the given environment is intended for the desired robot configuration. Because each robot is assigned a unique ID number, multiple instances of identical robots can be instantiated within the simulation without error. * **Self-Enclosed Abstraction**: For a given task and environment, any information relevant to the specific robot instance can be found within the properties and methods within that instance. This means that each robot is responsible for directly setting its initial state within the simulation at the start of each episode, and also directly controls the robot in simulation via torques outputted by its controller's transformed actions. @@ -17,7 +17,7 @@ The high-level features of **robosuite**'s robots are described as follows: Below, we discuss the usage and functionality of the robots over the course of its program lifetime. #### Initialization -During environment creation (`suite.make(...)`), individual robots are both instantiated and initialized. The desired RobotModel, GripperModel(s), and Controller(s) (where multiple can be specified for bimanual robots) are loaded into each robot, with the models being passed into the environment to compose the final MuJoCo simulation object. Each robot is then set to its initial state. +During environment creation (`suite.make(...)`), individual robots are both instantiated and initialized. The desired RobotModel, MountModel, and Controller(s) (where multiple and / or additional models may be specified, e.g. for manipulator bimanual robots) are loaded into each robot, with the models being passed into the environment to compose the final MuJoCo simulation object. Each robot is then set to its initial state. #### Runtime During a given simulation episode (each `env.step(...)` call), the environment will receive a set of actions and distribute them accordingly to each robot, according to their respective action spaces. Each robot then converts these actions into low-level torques via their respective controllers, and directly execute these torques in the simulation. At the conclusion of the environment step, each robot will pass its set of robot-specific observations to the environment, which will then concatenate and append additional task-level observations before passing them as output from the `env.step(...)` call. @@ -27,7 +27,10 @@ At any given time, each robot has a set of `properties` whose real-time values c ## Models -**robosuite** currently supports seven commercially-available robot models. We briefly describe each individual model along with its features below: +**robosuite** is designed to be generalizable to multiple robotic domains. The current release focuses on manipulator robots. + +### Manipulators +**robosuite** currently supports seven commercially-available manipulator robot models. We briefly describe each individual model along with its features below: #### Panda ![panda_robot](../images/models/robot_model_Panda.png) diff --git a/docs/modules/sensors.md b/docs/modules/sensors.md index e9af9a1cd6..c94ec814df 100644 --- a/docs/modules/sensors.md +++ b/docs/modules/sensors.md @@ -1,22 +1,80 @@ # Sensors -The simulator generates virtual physical signals as response to a robot's interactions. Virtual signals include images, force-torque measurements (from a force-torque sensor like the one included by default in the wrist of all [Gripper models](../modeling/gripper_model)), pressure signals (e.g. from a sensor on the robot's finger or on the environment), etc. Sensors (except cameras and joint sensors) are accessed via the function `get_sensor_measurement` provided the name of the sensor. +Sensors are an important aspect of **robosuite**, and encompass an agent's feedback from interaction with the environment. Mujoco provides low-level APIs to directly interface with raw simulation data, though we provide more a more realistic interface via the `Observable` class API to model obtained sensory information. + +#### Mujoco-Native Sensors + +The simulator generates virtual physical signals as response to a robot's interactions. Virtual signals include images, force-torque measurements (from a force-torque sensor like the one included by default in the wrist of all [Gripper models](../modeling/gripper_model)), pressure signals (e.g. from a sensor on the robot's finger or on the environment), etc. Raw sensor information (except cameras and joint sensors) can be accessed via the function `get_sensor_measurement` provided the name of the sensor. Joint sensors provide information about the state of each robot's joint including position and velocity. In MuJoCo these are not measured by sensors, but resolved and set by the simulator as the result of the actuation forces. Therefore, they are not accessed through the common `get_sensor_measurement` function but as properties of the [Robot simulation API](../simulation/robot), i.e., `_joint_positions` and `_joint_velocities`. Cameras bundle a name to a set of properties to render images of the environment such as the pose and pointing direction, field of view, and resolution. Inheriting from MuJoCo, cameras are defined in the [robot](../modeling/robot_model) and [arena models](../modeling/arena) and can be attached to any body. Images, as they would be generated from the cameras, are not accessed through `get_sensor_measurement` but via the renderer (see below). In a common user pipeline, images are not queried directly; we specify one or several cameras we want to use images from when we create the environment, and the images are generated and appended automatically to the observation dictionary. -#### Renderers +#### Observables + +**robosuite** provides a realistic, customizable interface via the [Observable](../../../robosuite/utils/observables.py) class API. Observables model realistic sensor sampling, in which ground truth data is sampled (`sensor`), passed through a corrupting function (`corrupter`), and then finally passed through a filtering function (`filter`). Moreover, each observable has its own `sampling_rate` and `delayer` function which simulates sensor delay. While default values are used to instantiate each observable during environment creation, each of these components can be modified by the user at runtime using `env.modify_observable(...)` . Moreover, each observable is assigned a modality, and are grouped together in the returned observation dictionary during the `env.step()` call. For example, if an environment consists of camera observations and a single robot's proprioceptive observations, the observation dict structure might look as follows: + +```python +{ + "frontview_image": np.array(...), # this has modality "image" + "frontview_depth": np.array(...), # this has modality "image" + "robot0_joint_pos": np.array(...), # this has modality "robot0_proprio" + "robot0_gripper_pos": np.array(...), # this has modality "robot0_proprio" + "image-state": np.array(...), # this is a concatenation of all image observations + "robot0_proprio-state": np.array(...), # this is a concatenation of all robot0_proprio observations +} +``` + +Note that for memory efficiency the `image-state` is not returned by default (this can be toggled in `robosuite/utils/macros.py`). + +We showcase how the `Observable` functionality can be used to model sensor corruption and delay via [demo_sensor_corruption.py](../../../robosuite/demos/demo_sensor_corruption.py). We also highlight that each of the `sensor`, `corrupter`, and `filter` functions can be arbitrarily specified to suit the end-user's usage. For example, a common use case for these observables is to keep track of sampled values from a sensor operating at a higher frequency than the environment step (control) frequency. In this case, the `filter` function can be leveraged to keep track of the real-time sensor values as they're being sampled. We provide a minimal script showcasing this ability below: + +```python +import robosuite as suite +import numpy as np +from robosuite.utils.buffers import RingBuffer + +# Create env instance +control_freq = 10 +env = suite.make("Lift", robots="Panda", has_offscreen_renderer=False, use_camera_obs=False, control_freq=control_freq) -Renderers compute virtual images of the environment based on the properties defined in the cameras. In **robosuite**, the user can select one of these two renderers: +# Define a ringbuffer to store joint position values +buffer = RingBuffer(dim=env.robots[0].robot_model.dof, length=10) -##### MjViewer +# Create a function that we'll use as the "filter" for the joint position Observable +# This is a pass-through operation, but we record the value every time it gets called +# As per the Observables API, this should take in an arbitrary numeric and return the same type / shape +def filter_fcn(corrupted_value): + # Record the inputted value + buffer.push(corrupted_value) + # Return this value (no-op performed) + return corrupted_value -This is the default renderer from [mujoco-py](https://openai.github.io/mujoco-py/build/html/reference.html#mjviewer-3d-rendering). Based on [OpenGL](https://www.opengl.org/), our assets and environment definitions have been tuned to look good with this renderer. +# Now, let's enable the joint position Observable with this filter function +env.modify_observable( + observable_name="robot0_joint_pos", + attribute="filter", + modifier=filter_fcn, +) - - +# Let's also increase the sampling rate to showcase the Observable's ability to update multiple times per env step +obs_sampling_freq = control_freq * 4 +env.modify_observable( + observable_name="robot0_joint_pos", + attribute="sampling_rate", + modifier=obs_sampling_freq, +) -##### PyGame +# Take a single environment step with positive joint velocity actions +arm_action = np.ones(env.robots[0].robot_model.dof) * 1.0 +gripper_action = [1] +action = np.concatenate([arm_action, gripper_action]) +env.step(action) -[PyGame](https://www.pygame.org/news) is a simple renderer that serves also as an alternative to MjViewer. A limitation of PyGame is that it can only render on-screen, limiting its applicability to train on computing clusters. However, it is useful for visualizing the robots' behaviors in the system runtime where MjViewer is not supported. +# Now we can analyze what values were recorded +np.set_printoptions(precision=2) +print(f"\nPolicy Frequency: {control_freq}, Observable Sampling Frequency: {obs_sampling_freq}") +print(f"Number of recorded samples after 1 policy step: {buffer._size}\n") +for i in range(buffer._size): + print(f"Recorded value {i}: {buffer.buf[i]}") +``` \ No newline at end of file diff --git a/docs/simulation/environment.rst b/docs/simulation/environment.rst index b0899b9fdb..767c3f4212 100644 --- a/docs/simulation/environment.rst +++ b/docs/simulation/environment.rst @@ -11,16 +11,25 @@ Base Environment .. autoclass:: robosuite.environments.base.MujocoEnv .. automethod:: initialize_time + .. automethod:: set_model_postprocessor .. automethod:: reset .. automethod:: step .. automethod:: reward .. automethod:: render .. automethod:: observation_spec - .. autoproperty:: action_spec - .. autoproperty:: action_dim + .. automethod:: clear_objects + .. automethod:: visualize .. automethod:: reset_from_xml_string - .. automethod:: find_contacts + .. automethod:: check_contact + .. automethod:: get_contacts + .. automethod:: modify_observable .. automethod:: close + .. autoproperty:: observation_modalities + .. autoproperty:: observation_names + .. autoproperty:: enabled_observables + .. autoproperty:: active_observables + .. autoproperty:: action_spec + .. autoproperty:: action_dim Robot Environment @@ -28,13 +37,15 @@ Robot Environment .. autoclass:: robosuite.environments.robot_env.RobotEnv - .. automethod:: move_indicator - .. automethod:: _pre_action - .. automethod:: _post_action - .. automethod:: _get_observation - .. automethod:: _check_gripper_contact - .. automethod:: _check_arm_contact - .. automethod:: _check_q_limits - .. automethod:: _visualization .. automethod:: _load_robots - .. automethod:: _check_robot_configuration \ No newline at end of file + .. automethod:: _check_robot_configuration + + +Manipulator Environment +----------------------- + +.. autoclass:: robosuite.environments.manipulation.manipulation_env.ManipulationEnv + + .. automethod:: _check_grasp + .. automethod:: _gripper_to_target + .. automethod:: _visualize_gripper_to_target \ No newline at end of file diff --git a/docs/simulation/robot.rst b/docs/simulation/robot.rst index 406b2e2f34..54511d0ad0 100644 --- a/docs/simulation/robot.rst +++ b/docs/simulation/robot.rst @@ -2,8 +2,7 @@ Robot ===== The Robot class defines a simulation object encapsulating a robot model, gripper model, and controller. Robosuite uses -class extensions of this base class, namely, SingleArm and Bimanual classes representing the two different types of -supported robots. +class extensions of this base class to model different robotic domains. The current release focuses on manipulation, and includes a Manipulator class, which itself is extended by SingleArm and Bimanual classes representing the two different types of supported robots. Base Robot ---------- @@ -14,35 +13,57 @@ Base Robot .. automethod:: reset_sim .. automethod:: reset .. automethod:: setup_references + .. automethod:: setup_observables .. automethod:: control + .. automethod:: check_q_limits + .. automethod:: visualize + .. automethod:: pose_in_base_from_name + .. automethod:: set_robot_joint_position .. automethod:: get_sensor_measurement .. autoproperty:: action_limits .. autoproperty:: torque_limits .. autoproperty:: action_dim .. autoproperty:: dof + .. autoproperty:: js_energy .. autoproperty:: joint_indexes .. autoproperty:: _joint_positions .. autoproperty:: _joint_velocities +Manipulator Robot +----------------- + +.. autoclass:: robosuite.robots.manipulator.Manipulator + + .. automethod:: grip_action + .. autoproperty:: ee_ft_integral + .. autoproperty:: ee_force + .. autoproperty:: ee_torque + .. autoproperty:: _hand_pose + .. autoproperty:: _hand_quat + .. autoproperty:: _hand_total_velocity + .. autoproperty:: _hand_pos + .. autoproperty:: _hand_orn + .. autoproperty:: _hand_vel + .. autoproperty:: _hand_ang_vel + + SingleArm Robot --------------- .. autoclass:: robosuite.robots.single_arm.SingleArm .. automethod:: control - .. automethod:: grip_action - .. autoproperty:: js_energy .. autoproperty:: ee_ft_integral .. autoproperty:: ee_force .. autoproperty:: ee_torque - .. autoproperty:: _right_hand_pose - .. autoproperty:: _right_hand_quat - .. autoproperty:: _right_hand_total_velocity - .. autoproperty:: _right_hand_pos - .. autoproperty:: _right_hand_orn - .. autoproperty:: _right_hand_vel - .. autoproperty:: _right_hand_ang_vel + .. autoproperty:: _hand_pose + .. autoproperty:: _hand_quat + .. autoproperty:: _hand_total_velocity + .. autoproperty:: _hand_pos + .. autoproperty:: _hand_orn + .. autoproperty:: _hand_vel + .. autoproperty:: _hand_ang_vel Bimanual Robot @@ -51,9 +72,6 @@ Bimanual Robot .. autoclass:: robosuite.robots.bimanual.Bimanual .. automethod:: control - .. automethod:: grip_action - .. autoproperty:: arms - .. autoproperty:: js_energy .. autoproperty:: ee_ft_integral .. autoproperty:: ee_force .. autoproperty:: ee_torque From 37a7e5e3403ded81f7b4b2d5c5f4ae9e034041dc Mon Sep 17 00:00:00 2001 From: Yuke Zhu Date: Wed, 3 Feb 2021 14:54:37 -0600 Subject: [PATCH 04/16] update sensor realism demo --- docs/demos.md | 5 +++++ docs/overview.md | 4 ++-- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/docs/demos.md b/docs/demos.md index 91a6e44095..e98ac7c103 100644 --- a/docs/demos.md +++ b/docs/demos.md @@ -46,6 +46,11 @@ The `demo_domain_randomization.py` script showcases our domain randomization fun ![collection of grippers](images/domain_randomization.png) + +### Sensor Realism +The `demo_sensor_corruption.py` script shows how the [Observable](modules/sensors) functionality can be used to model sensor corruption and delay. The [Observable](modules/sensors) class provides easy-to-use interface for simulating real-world sensor noises, variable sensor delays, and sampling frequencies of realistic sensors. These techniques are useful for improving the generalization and robustness of the policies trained on robosuite, facilitating sim2real transfer to the real hardware. For example, see Appendix B of [Zhu et al. RSS'2018](https://arxiv.org/abs/1802.09564) and Section V of [Tan et al. RSS'2018](https://arxiv.org/abs/1804.10332). + + ### Gripper Selection The `demo_gripper_selection.py` script shows you how to select gripper for an environment. This is controlled by `gripper_type` keyword argument. The set of all grippers is defined by the global variable `robosuite.ALL_GRIPPERS`. diff --git a/docs/overview.md b/docs/overview.md index 2fd7a498fb..eeea6cf596 100644 --- a/docs/overview.md +++ b/docs/overview.md @@ -2,7 +2,7 @@ ![gallery of_environments](images/gallery.png) -**robosuite** is a simulation framework powered by the [MuJoCo](http://mujoco.org/) physics engine for robot learning. It also offers a suite of benchmark environments for reproducible research. The release or robosuite v1.0 features manipulation tasks with supports of procedural generation, advanced controllers, teleoperation, etc. This project is part of the broader [Advancing Robot Intelligence through Simulated Environments (ARISE) Initiative](https://github.com/ARISE-Initiative), with the aim of lowering the barriers of entry for cutting-edge research at the intersection of AI and Robotics. +**robosuite** is a simulation framework powered by the [MuJoCo](http://mujoco.org/) physics engine for robot learning. It also offers a suite of benchmark environments for reproducible research. The release or robosuite v1.2 features manipulation tasks with supports of procedural generation, advanced controllers, teleoperation, etc. This project is part of the broader [Advancing Robot Intelligence through Simulated Environments (ARISE) Initiative](https://github.com/ARISE-Initiative), with the aim of lowering the barriers of entry for cutting-edge research at the intersection of AI and Robotics. Data-driven algorithms, such as reinforcement learning and imitation learning, provide a powerful and generic tool in robotics. These learning paradigms, fueled by new advances in deep learning, have achieved some exciting successes in a variety of robot control problems. However, the challenges of reproducibility and the limited accessibility of robot hardware (especially during a pandemic) have impaired research progress. The overarching goal of **robosuite** is to provide researchers with: @@ -10,7 +10,7 @@ Data-driven algorithms, such as reinforcement learning and imitation learning, p * a modular design that offers great flexibility to design new robot simulation environments; * a high-quality implementation of robot controllers and off-the-shelf learning algorithms to lower the barriers to entry. -This framework was originally developed since late 2017 by researchers in [Stanford Vision and Learning Lab](http://svl.stanford.edu/) (SVL) as an internal tool for robot learning research. Now it is actively maintained and used for robotics research projects in SVL and the [UT-Austin Robot Perception and Learning (RPL) Lab](https://www.cs.utexas.edu/~yukez/). We welcome community contributions to this project. For details please check out our [contributing guidelines](https://github.com/ARISE-Initiative/robosuite/blob/master/CONTRIBUTING.md). +This framework was originally developed since late 2017 by researchers in [Stanford Vision and Learning Lab](http://svl.stanford.edu/) (SVL) as an internal tool for robot learning research. Now it is actively maintained and used for robotics research projects in SVL and the [UT-Austin Robot Perception and Learning (RPL) Lab](http://rpl.cs.utexas.edu). We welcome community contributions to this project. For details please check out our [contributing guidelines](https://github.com/ARISE-Initiative/robosuite/blob/master/CONTRIBUTING.md). This release of **robosuite** contains seven robot models, eight gripper models, six controller modes, and nine standardized tasks. It also offers a modular design of APIs for building new environments with procedural generation. We highlight these primary features below: From d2bf6d45ee05432a82a685ef6360ad3246cccdd2 Mon Sep 17 00:00:00 2001 From: Yuke Zhu Date: Thu, 4 Feb 2021 20:19:56 -0600 Subject: [PATCH 05/16] add new sources and restructure docs --- docs/index.rst | 3 +- docs/modeling/gripper_model.rst | 17 --- docs/modeling/manipulator_model.rst | 16 --- docs/modeling/mount_model.rst | 15 --- docs/modeling/mujoco_model.rst | 4 +- docs/modeling/robot_model.rst | 49 +++++++- docs/modules/controllers.ipynb | 4 +- docs/modules/robots.md | 2 +- docs/simulation/robot.rst | 5 +- .../robosuite.controllers.interpolators.rst | 29 +++++ docs/source/robosuite.controllers.rst | 35 ++++-- docs/source/robosuite.devices.rst | 19 ++- .../robosuite.environments.manipulation.rst | 109 ++++++++++++++++++ docs/source/robosuite.environments.rst | 81 +++---------- docs/source/robosuite.models.arenas.rst | 25 ++-- docs/source/robosuite.models.grippers.rst | 35 ++++-- docs/source/robosuite.models.mounts.rst | 53 +++++++++ .../robosuite.models.objects.composite.rst | 29 +++++ ...obosuite.models.objects.composite_body.rst | 21 ++++ .../robosuite.models.objects.primitive.rst | 45 ++++++++ docs/source/robosuite.models.objects.rst | 29 ++++- .../robosuite.models.robots.manipulators.rst | 77 +++++++++++++ docs/source/robosuite.models.robots.rst | 61 ++-------- docs/source/robosuite.models.rst | 21 +++- docs/source/robosuite.models.tasks.rst | 21 +++- docs/source/robosuite.robots.rst | 27 ++++- docs/source/robosuite.rst | 2 +- docs/source/robosuite.utils.rst | 69 +++++++++-- docs/source/robosuite.wrappers.rst | 21 +++- 29 files changed, 689 insertions(+), 235 deletions(-) delete mode 100644 docs/modeling/gripper_model.rst delete mode 100644 docs/modeling/manipulator_model.rst delete mode 100644 docs/modeling/mount_model.rst create mode 100644 docs/source/robosuite.controllers.interpolators.rst create mode 100644 docs/source/robosuite.environments.manipulation.rst create mode 100644 docs/source/robosuite.models.mounts.rst create mode 100644 docs/source/robosuite.models.objects.composite.rst create mode 100644 docs/source/robosuite.models.objects.composite_body.rst create mode 100644 docs/source/robosuite.models.objects.primitive.rst create mode 100644 docs/source/robosuite.models.robots.manipulators.rst diff --git a/docs/index.rst b/docs/index.rst index a9cbb5cab5..4a0ea8d1b8 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -26,6 +26,7 @@ Welcome to robosuite's documentation! modules/environments modules/sensors modules/devices + modules/renderers .. toctree:: :maxdepth: 1 @@ -40,8 +41,8 @@ Welcome to robosuite's documentation! :maxdepth: 1 :caption: Modeling API + modeling/mujoco_model modeling/robot_model - modeling/gripper_model modeling/object_model modeling/arena modeling/task diff --git a/docs/modeling/gripper_model.rst b/docs/modeling/gripper_model.rst deleted file mode 100644 index e5bde3daeb..0000000000 --- a/docs/modeling/gripper_model.rst +++ /dev/null @@ -1,17 +0,0 @@ -Gripper Model -============= - -The `GripperModel` class serves as a direct intermediary class that reads in information from a corresponding gripper XML file and also contains relevant hard-coded information from that XML. In conjunction with the `ManipulatorModel` class, this serves as the core modeling component of the higher-level `Manipulator` class used in simulation. - -Base Gripper Model ------------------- - -.. autoclass:: robosuite.models.grippers.gripper_model.GripperModel - - .. automethod:: format_action - .. autoproperty:: speed - .. autoproperty:: dof - .. autoproperty:: init_qpos - .. autoproperty:: _important_sites - .. autoproperty:: _important_geoms - .. autoproperty:: _important_sensors diff --git a/docs/modeling/manipulator_model.rst b/docs/modeling/manipulator_model.rst deleted file mode 100644 index 7fbc28e343..0000000000 --- a/docs/modeling/manipulator_model.rst +++ /dev/null @@ -1,16 +0,0 @@ -Manipulator Model -================= - -The `ManipulatorModel` class extends from the base `RobotModel` class, and represents an armed, mounted robot with an optional gripper attached to its end effector. In conjunction with the corresponding `GripperModel` class and `MountModel` class, this serves as the core modeling component of the higher-level `Manipulator` class used in simulation. - -Base Manipulator Model ----------------------- - -.. autoclass:: robosuite.models.robots.manipulators.manipulator_model.ManipulatorModel - - .. automethod:: add_gripper - .. autoproperty:: default_gripper - .. autoproperty:: arm_type - .. autoproperty:: base_xpos_offset - .. autoproperty:: _important_sites - .. autoproperty:: _eef_name \ No newline at end of file diff --git a/docs/modeling/mount_model.rst b/docs/modeling/mount_model.rst deleted file mode 100644 index 9379246c41..0000000000 --- a/docs/modeling/mount_model.rst +++ /dev/null @@ -1,15 +0,0 @@ -Mount Model -=========== - -The `MountModel` class serves as a direct intermediary class that reads in information from a corresponding mount XML file and also contains relevant hard-coded information from that XML. In conjunction with the `RobotModel` class, this serves as the core modeling component of the higher-level Robot class used in simulation. - -Base Mount Model ----------------- - -.. autoclass:: robosuite.models.mounts.mount_model.MountModel - - .. autoproperty:: top_offset - .. autoproperty:: horizontal_radius - .. autoproperty:: _important_sites - .. autoproperty:: _important_geoms - .. autoproperty:: _important_sensors diff --git a/docs/modeling/mujoco_model.rst b/docs/modeling/mujoco_model.rst index aceb627799..d496120124 100644 --- a/docs/modeling/mujoco_model.rst +++ b/docs/modeling/mujoco_model.rst @@ -9,7 +9,7 @@ Base Mujoco Model .. autoclass:: robosuite.models.base.MujocoModel .. automethod:: correct_naming - .. automethod:: set_site_visibility + .. automethod:: set_sites_visibility .. automethod:: exclude_from_prefixing .. autoproperty:: name .. autoproperty:: naming_prefix @@ -35,4 +35,4 @@ XML Mujoco Model .. autoclass:: robosuite.models.base.MujocoXMLModel .. autoproperty:: base_offset - .. autoproperty:: contact_geom_rgba \ No newline at end of file + .. autoproperty:: contact_geom_rgba diff --git a/docs/modeling/robot_model.rst b/docs/modeling/robot_model.rst index e96efa810a..d3b3288cec 100644 --- a/docs/modeling/robot_model.rst +++ b/docs/modeling/robot_model.rst @@ -1,12 +1,11 @@ Robot Model =========== +Robot Model +----------- The `RobotModel` class serves as a direct intermediary class that reads in information from a corresponding robot XML file and also contains relevant hard-coded information from that XML. This represents an arbitrary robot optionally equipped with a mount via the `MountModel` class and is the core modeling component of the higher-level `Robot` class used in simulation. -Base Robot Model ----------------- - .. autoclass:: robosuite.models.robots.robot_model.RobotModel .. automethod:: set_base_xpos @@ -21,4 +20,46 @@ Base Robot Model .. autoproperty:: _horizontal_radius .. autoproperty:: _important_sites .. autoproperty:: _important_geoms - .. autoproperty:: _important_sensors \ No newline at end of file + .. autoproperty:: _important_sensors + + +Manipulator Model +----------------- +The `ManipulatorModel` class extends from the base `RobotModel` class, and represents an armed, mounted robot with an optional gripper attached to its end effector. In conjunction with the corresponding `GripperModel` class and `MountModel` class, this serves as the core modeling component of the higher-level `Manipulator` class used in simulation. + +.. autoclass:: robosuite.models.robots.manipulators.manipulator_model.ManipulatorModel + + .. automethod:: add_gripper + .. autoproperty:: default_gripper + .. autoproperty:: arm_type + .. autoproperty:: base_xpos_offset + .. autoproperty:: _important_sites + .. autoproperty:: _eef_name + + +Gripper Model +------------- +The `GripperModel` class serves as a direct intermediary class that reads in information from a corresponding gripper XML file and also contains relevant hard-coded information from that XML. In conjunction with the `ManipulatorModel` class, this serves as the core modeling component of the higher-level `Manipulator` class used in simulation. + +.. autoclass:: robosuite.models.grippers.gripper_model.GripperModel + + .. automethod:: format_action + .. autoproperty:: speed + .. autoproperty:: dof + .. autoproperty:: init_qpos + .. autoproperty:: _important_sites + .. autoproperty:: _important_geoms + .. autoproperty:: _important_sensors + + +Mount Model +----------- +The `MountModel` class serves as a direct intermediary class that reads in information from a corresponding mount XML file and also contains relevant hard-coded information from that XML. In conjunction with the `RobotModel` class, this serves as the core modeling component of the higher-level Robot class used in simulation. + +.. autoclass:: robosuite.models.mounts.mount_model.MountModel + + .. autoproperty:: top_offset + .. autoproperty:: horizontal_radius + .. autoproperty:: _important_sites + .. autoproperty:: _important_geoms + .. autoproperty:: _important_sensors diff --git a/docs/modules/controllers.ipynb b/docs/modules/controllers.ipynb index 8c029b6880..f38eb6e608 100644 --- a/docs/modules/controllers.ipynb +++ b/docs/modules/controllers.ipynb @@ -106,7 +106,7 @@ "\n", "Action Dimensions (not including gripper): `6`\n", "\n", - "In the `OSC_POSE` controller, the desired value is the 6D pose (position and orientation) of a controlled frame. We follow the formalism from [\\[Khatib87\\]](https://ieeexplore.ieee.org/document/1087068). Our control frame is always the `eef_site` defined in the [Gripper Model](../modeling/gripper_model.html), placed at the end of the last link for robots without gripper or between the fingers for robots with gripper. The operational space control framework (OSC) computes the necessary joint torques to minimize the error between the desired and the current pose of the `eef_site` with the minimal kinematic energy. \n", + "In the `OSC_POSE` controller, the desired value is the 6D pose (position and orientation) of a controlled frame. We follow the formalism from [\\[Khatib87\\]](https://ieeexplore.ieee.org/document/1087068). Our control frame is always the `eef_site` defined in the [Gripper Model](../modeling/robot_model.html#gripper-model), placed at the end of the last link for robots without gripper or between the fingers for robots with gripper. The operational space control framework (OSC) computes the necessary joint torques to minimize the error between the desired and the current pose of the `eef_site` with the minimal kinematic energy. \n", "\n", "Given a desired pose $\\mathbf{x}_{\\mathit{des}}$ and the current end-effector pose, , we first compute the end-effector acceleration that would help minimize the error between both, assumed. PD (proportional-derivative) control schema to improve convergence and stability. For that, we first decompose into a desired position, $p_d \\in \\mathbb{R}^3$, and a desired orientation, $R_d \\in \\mathbb{SO}(3)$. The end-effector acceleration to minimize the error should increase with the difference between desired end-effector pose and current pose, $p$ and $R$ (proportional term), and decrease with the current end-effector velocity, $v$ and $\\omega$ (derivative term).\n", "\n", @@ -143,7 +143,7 @@ "\n", "\n", "## Configurations\n", - "The config directory (found within the [Controllers](../source/robosuite.controllers) module) provides a set of default configuration files that hold default examples of parameters relevant to individual controllers. Note that when creating your controller config templates of a certain type of controller, the listed parameters in the default example are required and should be specified accordingly.\n", + "The config directory (found within the [Controllers](../source/robosuite.controllers.html) module) provides a set of default configuration files that hold default examples of parameters relevant to individual controllers. Note that when creating your controller config templates of a certain type of controller, the listed parameters in the default example are required and should be specified accordingly.\n", "\n", "Note: Each robot has its own default controller configuration which is called by default unless a different controller config is called.\n", "\n", diff --git a/docs/modules/robots.md b/docs/modules/robots.md index d2803a5e9d..11f237256a 100644 --- a/docs/modules/robots.md +++ b/docs/modules/robots.md @@ -2,7 +2,7 @@ ![robot_overview_diagram](../images/robot_module.png) -**Robots** are a key component in **robosuite**, and serve as the embodiment of a given agent as well as the central interaction point within an environment and key interface to MuJoCo for the robot-related state and control. **robosuite** captures this level of abstraction with the [Robot](../simulation/robot)-based classes, with support for both single-armed and bimanual variations. In turn, the Robot class is centrally defined by a [RobotModel](../modeling/robot_model), [MountModel](../modeling/mount_model), and [Controller(s)](../simulation/controller). Subclasses of the `RobotModel` class may also include additional models as well; for example, the [ManipulatorModel](../modeling/manipulator_model) class also includes [GripperModel(s)](../modeling/gripper_model) (with no gripper being represented by a dummy class). +**Robots** are a key component in **robosuite**, and serve as the embodiment of a given agent as well as the central interaction point within an environment and key interface to MuJoCo for the robot-related state and control. **robosuite** captures this level of abstraction with the [Robot](../simulation/robot)-based classes, with support for both single-armed and bimanual variations. In turn, the Robot class is centrally defined by a [RobotModel](../modeling/robot_model), [MountModel](../modeling/robot_model.html#mount-model), and [Controller(s)](../simulation/controller). Subclasses of the `RobotModel` class may also include additional models as well; for example, the [ManipulatorModel](../modeling/robot_model.html#manipulator-model) class also includes [GripperModel(s)](../modeling/robot_model.html#gripper-model) (with no gripper being represented by a dummy class). The high-level features of **robosuite**'s robots are described as follows: diff --git a/docs/simulation/robot.rst b/docs/simulation/robot.rst index 54511d0ad0..2b819b0ba3 100644 --- a/docs/simulation/robot.rst +++ b/docs/simulation/robot.rst @@ -1,8 +1,7 @@ Robot ===== -The Robot class defines a simulation object encapsulating a robot model, gripper model, and controller. Robosuite uses -class extensions of this base class to model different robotic domains. The current release focuses on manipulation, and includes a Manipulator class, which itself is extended by SingleArm and Bimanual classes representing the two different types of supported robots. +The `Robot` class defines a simulation object encapsulating a robot model, gripper model, and controller. Robosuite uses class extensions of this base class to model different robotic domains. The current release focuses on manipulation, and includes a `Manipulator` class, which itself is extended by `SingleArm` and `Bimanual` classes representing the two different types of supported robots. Base Robot ---------- @@ -18,7 +17,7 @@ Base Robot .. automethod:: check_q_limits .. automethod:: visualize .. automethod:: pose_in_base_from_name - .. automethod:: set_robot_joint_position + .. automethod:: set_robot_joint_positions .. automethod:: get_sensor_measurement .. autoproperty:: action_limits .. autoproperty:: torque_limits diff --git a/docs/source/robosuite.controllers.interpolators.rst b/docs/source/robosuite.controllers.interpolators.rst new file mode 100644 index 0000000000..0c8afe8ace --- /dev/null +++ b/docs/source/robosuite.controllers.interpolators.rst @@ -0,0 +1,29 @@ +robosuite.controllers.interpolators package +=========================================== + +Submodules +---------- + +robosuite.controllers.interpolators.base\_interpolator module +------------------------------------------------------------- + +.. automodule:: robosuite.controllers.interpolators.base_interpolator + :members: + :undoc-members: + :show-inheritance: + +robosuite.controllers.interpolators.linear\_interpolator module +--------------------------------------------------------------- + +.. automodule:: robosuite.controllers.interpolators.linear_interpolator + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: robosuite.controllers.interpolators + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/robosuite.controllers.rst b/docs/source/robosuite.controllers.rst index 7c59ea7143..8ef16210c0 100644 --- a/docs/source/robosuite.controllers.rst +++ b/docs/source/robosuite.controllers.rst @@ -1,7 +1,18 @@ -robosuite.controllers +robosuite.controllers package ============================= -robosuite.controllers.base\_controller +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + robosuite.controllers.interpolators + +Submodules +---------- + +robosuite.controllers.base\_controller module --------------------------------------------- .. automodule:: robosuite.controllers.base_controller @@ -9,7 +20,7 @@ robosuite.controllers.base\_controller :undoc-members: :show-inheritance: -robosuite.controllers.controller\_factory +robosuite.controllers.controller\_factory module ------------------------------------------------ .. automodule:: robosuite.controllers.controller_factory @@ -17,7 +28,7 @@ robosuite.controllers.controller\_factory :undoc-members: :show-inheritance: -robosuite.controllers.ik +robosuite.controllers.ik module ------------------------------- .. automodule:: robosuite.controllers.ik @@ -25,7 +36,7 @@ robosuite.controllers.ik :undoc-members: :show-inheritance: -robosuite.controllers.joint\_pos +robosuite.controllers.joint\_pos module --------------------------------------- .. automodule:: robosuite.controllers.joint_pos @@ -33,7 +44,7 @@ robosuite.controllers.joint\_pos :undoc-members: :show-inheritance: -robosuite.controllers.joint\_tor +robosuite.controllers.joint\_tor module --------------------------------------- .. automodule:: robosuite.controllers.joint_tor @@ -41,7 +52,7 @@ robosuite.controllers.joint\_tor :undoc-members: :show-inheritance: -robosuite.controllers.joint\_vel +robosuite.controllers.joint\_vel module --------------------------------------- .. automodule:: robosuite.controllers.joint_vel @@ -49,10 +60,18 @@ robosuite.controllers.joint\_vel :undoc-members: :show-inheritance: -robosuite.controllers.osc +robosuite.controllers.osc module -------------------------------- .. automodule:: robosuite.controllers.osc :members: :undoc-members: :show-inheritance: + +Module contents +--------------- + +.. automodule:: robosuite.controllers + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/robosuite.devices.rst b/docs/source/robosuite.devices.rst index 1b59d2771a..14633059db 100644 --- a/docs/source/robosuite.devices.rst +++ b/docs/source/robosuite.devices.rst @@ -1,7 +1,10 @@ -robosuite.devices +robosuite.devices package ========================= -robosuite.devices.device +Submodules +---------- + +robosuite.devices.device module ------------------------------- .. automodule:: robosuite.devices.device @@ -9,7 +12,7 @@ robosuite.devices.device :undoc-members: :show-inheritance: -robosuite.devices.keyboard +robosuite.devices.keyboard module --------------------------------- .. automodule:: robosuite.devices.keyboard @@ -17,10 +20,18 @@ robosuite.devices.keyboard :undoc-members: :show-inheritance: -robosuite.devices.spacemouse +robosuite.devices.spacemouse module ----------------------------------- .. automodule:: robosuite.devices.spacemouse :members: :undoc-members: :show-inheritance: + +Module contents +--------------- + +.. automodule:: robosuite.devices + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/robosuite.environments.manipulation.rst b/docs/source/robosuite.environments.manipulation.rst new file mode 100644 index 0000000000..74f8b90fa6 --- /dev/null +++ b/docs/source/robosuite.environments.manipulation.rst @@ -0,0 +1,109 @@ +robosuite.environments.manipulation package +=========================================== + +Submodules +---------- + +robosuite.environments.manipulation.door module +----------------------------------------------- + +.. automodule:: robosuite.environments.manipulation.door + :members: + :undoc-members: + :show-inheritance: + +robosuite.environments.manipulation.lift module +----------------------------------------------- + +.. automodule:: robosuite.environments.manipulation.lift + :members: + :undoc-members: + :show-inheritance: + +robosuite.environments.manipulation.manipulation\_env module +------------------------------------------------------------ + +.. automodule:: robosuite.environments.manipulation.manipulation_env + :members: + :undoc-members: + :show-inheritance: + +robosuite.environments.manipulation.nut\_assembly module +-------------------------------------------------------- + +.. automodule:: robosuite.environments.manipulation.nut_assembly + :members: + :undoc-members: + :show-inheritance: + +robosuite.environments.manipulation.pick\_place module +------------------------------------------------------ + +.. automodule:: robosuite.environments.manipulation.pick_place + :members: + :undoc-members: + :show-inheritance: + +robosuite.environments.manipulation.single\_arm\_env module +----------------------------------------------------------- + +.. automodule:: robosuite.environments.manipulation.single_arm_env + :members: + :undoc-members: + :show-inheritance: + +robosuite.environments.manipulation.stack module +------------------------------------------------ + +.. automodule:: robosuite.environments.manipulation.stack + :members: + :undoc-members: + :show-inheritance: + +robosuite.environments.manipulation.two\_arm\_env module +-------------------------------------------------------- + +.. automodule:: robosuite.environments.manipulation.two_arm_env + :members: + :undoc-members: + :show-inheritance: + +robosuite.environments.manipulation.two\_arm\_handover module +------------------------------------------------------------- + +.. automodule:: robosuite.environments.manipulation.two_arm_handover + :members: + :undoc-members: + :show-inheritance: + +robosuite.environments.manipulation.two\_arm\_lift module +--------------------------------------------------------- + +.. automodule:: robosuite.environments.manipulation.two_arm_lift + :members: + :undoc-members: + :show-inheritance: + +robosuite.environments.manipulation.two\_arm\_peg\_in\_hole module +------------------------------------------------------------------ + +.. automodule:: robosuite.environments.manipulation.two_arm_peg_in_hole + :members: + :undoc-members: + :show-inheritance: + +robosuite.environments.manipulation.wipe module +----------------------------------------------- + +.. automodule:: robosuite.environments.manipulation.wipe + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: robosuite.environments.manipulation + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/robosuite.environments.rst b/docs/source/robosuite.environments.rst index 15538da26f..7af0754e1c 100644 --- a/docs/source/robosuite.environments.rst +++ b/docs/source/robosuite.environments.rst @@ -1,47 +1,26 @@ -robosuite.environments +robosuite.environments package ============================== -robosuite.environments.base ----------------------------------- +Subpackages +----------- -.. automodule:: robosuite.environments.base - :members: - :undoc-members: - :show-inheritance: +.. toctree:: + :maxdepth: 4 -robosuite.environments.door ----------------------------------- + robosuite.environments.manipulation -.. automodule:: robosuite.environments.door - :members: - :undoc-members: - :show-inheritance: +Submodules +---------- -robosuite.environments.lift +robosuite.environments.base module ---------------------------------- -.. automodule:: robosuite.environments.lift - :members: - :undoc-members: - :show-inheritance: - -robosuite.environments.nut\_assembly -------------------------------------------- - -.. automodule:: robosuite.environments.nut_assembly - :members: - :undoc-members: - :show-inheritance: - -robosuite.environments.pick\_place ------------------------------------------ - -.. automodule:: robosuite.environments.pick_place +.. automodule:: robosuite.environments.base :members: :undoc-members: :show-inheritance: -robosuite.environments.robot\_env +robosuite.environments.robot\_env module ---------------------------------------- .. automodule:: robosuite.environments.robot_env @@ -49,42 +28,10 @@ robosuite.environments.robot\_env :undoc-members: :show-inheritance: -robosuite.environments.stack ------------------------------------ - -.. automodule:: robosuite.environments.stack - :members: - :undoc-members: - :show-inheritance: - -robosuite.environments.two\_arm\_handover ------------------------------------------------ - -.. automodule:: robosuite.environments.two_arm_handover - :members: - :undoc-members: - :show-inheritance: - -robosuite.environments.two\_arm\_lift --------------------------------------------- - -.. automodule:: robosuite.environments.two_arm_lift - :members: - :undoc-members: - :show-inheritance: - -robosuite.environments.two\_arm\_peg\_in\_hole ------------------------------------------------------ - -.. automodule:: robosuite.environments.two_arm_peg_in_hole - :members: - :undoc-members: - :show-inheritance: - -robosuite.environments.wipe ----------------------------------- +Module contents +--------------- -.. automodule:: robosuite.environments.wipe +.. automodule:: robosuite.environments :members: :undoc-members: :show-inheritance: diff --git a/docs/source/robosuite.models.arenas.rst b/docs/source/robosuite.models.arenas.rst index 0498d1514b..ff42472ecf 100644 --- a/docs/source/robosuite.models.arenas.rst +++ b/docs/source/robosuite.models.arenas.rst @@ -1,7 +1,10 @@ -robosuite.models.arenas +robosuite.models.arenas package =============================== -robosuite.models.arenas.arena +Submodules +---------- + +robosuite.models.arenas.arena module ------------------------------------ .. automodule:: robosuite.models.arenas.arena @@ -9,7 +12,7 @@ robosuite.models.arenas.arena :undoc-members: :show-inheritance: -robosuite.models.arenas.bins\_arena +robosuite.models.arenas.bins\_arena module ------------------------------------------ .. automodule:: robosuite.models.arenas.bins_arena @@ -17,7 +20,7 @@ robosuite.models.arenas.bins\_arena :undoc-members: :show-inheritance: -robosuite.models.arenas.empty\_arena +robosuite.models.arenas.empty\_arena module ------------------------------------------- .. automodule:: robosuite.models.arenas.empty_arena @@ -25,7 +28,7 @@ robosuite.models.arenas.empty\_arena :undoc-members: :show-inheritance: -robosuite.models.arenas.pegs\_arena +robosuite.models.arenas.pegs\_arena module ------------------------------------------ .. automodule:: robosuite.models.arenas.pegs_arena @@ -33,7 +36,7 @@ robosuite.models.arenas.pegs\_arena :undoc-members: :show-inheritance: -robosuite.models.arenas.table\_arena +robosuite.models.arenas.table\_arena module ------------------------------------------- .. automodule:: robosuite.models.arenas.table_arena @@ -41,10 +44,18 @@ robosuite.models.arenas.table\_arena :undoc-members: :show-inheritance: -robosuite.models.arenas.wipe\_arena +robosuite.models.arenas.wipe\_arena module ------------------------------------------ .. automodule:: robosuite.models.arenas.wipe_arena :members: :undoc-members: :show-inheritance: + +Module contents +--------------- + +.. automodule:: robosuite.models.arenas + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/robosuite.models.grippers.rst b/docs/source/robosuite.models.grippers.rst index c90aec72e6..b65eac0b17 100644 --- a/docs/source/robosuite.models.grippers.rst +++ b/docs/source/robosuite.models.grippers.rst @@ -1,7 +1,10 @@ -robosuite.models.grippers +robosuite.models.grippers package ================================= -robosuite.models.grippers.gripper\_factory +Submodules +---------- + +robosuite.models.grippers.gripper\_factory module ------------------------------------------------- .. automodule:: robosuite.models.grippers.gripper_factory @@ -9,7 +12,7 @@ robosuite.models.grippers.gripper\_factory :undoc-members: :show-inheritance: -robosuite.models.grippers.gripper\_model +robosuite.models.grippers.gripper\_model module ----------------------------------------------- .. automodule:: robosuite.models.grippers.gripper_model @@ -17,7 +20,7 @@ robosuite.models.grippers.gripper\_model :undoc-members: :show-inheritance: -robosuite.models.grippers.gripper\_tester +robosuite.models.grippers.gripper\_tester module ------------------------------------------------ .. automodule:: robosuite.models.grippers.gripper_tester @@ -25,7 +28,7 @@ robosuite.models.grippers.gripper\_tester :undoc-members: :show-inheritance: -robosuite.models.grippers.jaco\_three\_finger\_gripper +robosuite.models.grippers.jaco\_three\_finger\_gripper module ------------------------------------------------------------- .. automodule:: robosuite.models.grippers.jaco_three_finger_gripper @@ -33,7 +36,7 @@ robosuite.models.grippers.jaco\_three\_finger\_gripper :undoc-members: :show-inheritance: -robosuite.models.grippers.null\_gripper +robosuite.models.grippers.null\_gripper module ---------------------------------------------- .. automodule:: robosuite.models.grippers.null_gripper @@ -41,7 +44,7 @@ robosuite.models.grippers.null\_gripper :undoc-members: :show-inheritance: -robosuite.models.grippers.panda\_gripper +robosuite.models.grippers.panda\_gripper module ----------------------------------------------- .. automodule:: robosuite.models.grippers.panda_gripper @@ -49,7 +52,7 @@ robosuite.models.grippers.panda\_gripper :undoc-members: :show-inheritance: -robosuite.models.grippers.rethink\_gripper +robosuite.models.grippers.rethink\_gripper module ------------------------------------------------- .. automodule:: robosuite.models.grippers.rethink_gripper @@ -57,7 +60,7 @@ robosuite.models.grippers.rethink\_gripper :undoc-members: :show-inheritance: -robosuite.models.grippers.robotiq\_140\_gripper +robosuite.models.grippers.robotiq\_140\_gripper module ------------------------------------------------------ .. automodule:: robosuite.models.grippers.robotiq_140_gripper @@ -65,7 +68,7 @@ robosuite.models.grippers.robotiq\_140\_gripper :undoc-members: :show-inheritance: -robosuite.models.grippers.robotiq\_85\_gripper +robosuite.models.grippers.robotiq\_85\_gripper module ----------------------------------------------------- .. automodule:: robosuite.models.grippers.robotiq_85_gripper @@ -73,7 +76,7 @@ robosuite.models.grippers.robotiq\_85\_gripper :undoc-members: :show-inheritance: -robosuite.models.grippers.robotiq\_three\_finger\_gripper +robosuite.models.grippers.robotiq\_three\_finger\_gripper module ---------------------------------------------------------------- .. automodule:: robosuite.models.grippers.robotiq_three_finger_gripper @@ -81,10 +84,18 @@ robosuite.models.grippers.robotiq\_three\_finger\_gripper :undoc-members: :show-inheritance: -robosuite.models.grippers.wiping\_gripper +robosuite.models.grippers.wiping\_gripper module ------------------------------------------------ .. automodule:: robosuite.models.grippers.wiping_gripper :members: :undoc-members: :show-inheritance: + +Module contents +--------------- + +.. automodule:: robosuite.models.grippers + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/robosuite.models.mounts.rst b/docs/source/robosuite.models.mounts.rst new file mode 100644 index 0000000000..bad7024063 --- /dev/null +++ b/docs/source/robosuite.models.mounts.rst @@ -0,0 +1,53 @@ +robosuite.models.mounts package +=============================== + +Submodules +---------- + +robosuite.models.mounts.mount\_factory module +--------------------------------------------- + +.. automodule:: robosuite.models.mounts.mount_factory + :members: + :undoc-members: + :show-inheritance: + +robosuite.models.mounts.mount\_model module +------------------------------------------- + +.. automodule:: robosuite.models.mounts.mount_model + :members: + :undoc-members: + :show-inheritance: + +robosuite.models.mounts.null\_mount module +------------------------------------------ + +.. automodule:: robosuite.models.mounts.null_mount + :members: + :undoc-members: + :show-inheritance: + +robosuite.models.mounts.rethink\_minimal\_mount module +------------------------------------------------------ + +.. automodule:: robosuite.models.mounts.rethink_minimal_mount + :members: + :undoc-members: + :show-inheritance: + +robosuite.models.mounts.rethink\_mount module +--------------------------------------------- + +.. automodule:: robosuite.models.mounts.rethink_mount + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: robosuite.models.mounts + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/robosuite.models.objects.composite.rst b/docs/source/robosuite.models.objects.composite.rst new file mode 100644 index 0000000000..3bfa093d68 --- /dev/null +++ b/docs/source/robosuite.models.objects.composite.rst @@ -0,0 +1,29 @@ +robosuite.models.objects.composite package +========================================== + +Submodules +---------- + +robosuite.models.objects.composite.hammer module +------------------------------------------------ + +.. automodule:: robosuite.models.objects.composite.hammer + :members: + :undoc-members: + :show-inheritance: + +robosuite.models.objects.composite.pot\_with\_handles module +------------------------------------------------------------ + +.. automodule:: robosuite.models.objects.composite.pot_with_handles + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: robosuite.models.objects.composite + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/robosuite.models.objects.composite_body.rst b/docs/source/robosuite.models.objects.composite_body.rst new file mode 100644 index 0000000000..701da2b47a --- /dev/null +++ b/docs/source/robosuite.models.objects.composite_body.rst @@ -0,0 +1,21 @@ +robosuite.models.objects.composite\_body package +================================================ + +Submodules +---------- + +robosuite.models.objects.composite\_body.hinged\_box module +----------------------------------------------------------- + +.. automodule:: robosuite.models.objects.composite_body.hinged_box + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: robosuite.models.objects.composite_body + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/robosuite.models.objects.primitive.rst b/docs/source/robosuite.models.objects.primitive.rst new file mode 100644 index 0000000000..0e16a8294e --- /dev/null +++ b/docs/source/robosuite.models.objects.primitive.rst @@ -0,0 +1,45 @@ +robosuite.models.objects.primitive package +========================================== + +Submodules +---------- + +robosuite.models.objects.primitive.ball module +---------------------------------------------- + +.. automodule:: robosuite.models.objects.primitive.ball + :members: + :undoc-members: + :show-inheritance: + +robosuite.models.objects.primitive.box module +--------------------------------------------- + +.. automodule:: robosuite.models.objects.primitive.box + :members: + :undoc-members: + :show-inheritance: + +robosuite.models.objects.primitive.capsule module +------------------------------------------------- + +.. automodule:: robosuite.models.objects.primitive.capsule + :members: + :undoc-members: + :show-inheritance: + +robosuite.models.objects.primitive.cylinder module +-------------------------------------------------- + +.. automodule:: robosuite.models.objects.primitive.cylinder + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: robosuite.models.objects.primitive + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/robosuite.models.objects.rst b/docs/source/robosuite.models.objects.rst index 1fbb8479c3..ade511ce5a 100644 --- a/docs/source/robosuite.models.objects.rst +++ b/docs/source/robosuite.models.objects.rst @@ -1,7 +1,20 @@ -robosuite.models.objects +robosuite.models.objects package ================================ -robosuite.models.objects.generated\_objects +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + robosuite.models.objects.composite + robosuite.models.objects.composite_body + robosuite.models.objects.primitive + +Submodules +---------- + +robosuite.models.objects.generated\_objects module -------------------------------------------------- .. automodule:: robosuite.models.objects.generated_objects @@ -9,7 +22,7 @@ robosuite.models.objects.generated\_objects :undoc-members: :show-inheritance: -robosuite.models.objects.objects +robosuite.models.objects.objects module --------------------------------------- .. automodule:: robosuite.models.objects.objects @@ -17,10 +30,18 @@ robosuite.models.objects.objects :undoc-members: :show-inheritance: -robosuite.models.objects.xml\_objects +robosuite.models.objects.xml\_objects module -------------------------------------------- .. automodule:: robosuite.models.objects.xml_objects :members: :undoc-members: :show-inheritance: + +Module contents +--------------- + +.. automodule:: robosuite.models.objects + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/robosuite.models.robots.manipulators.rst b/docs/source/robosuite.models.robots.manipulators.rst new file mode 100644 index 0000000000..9f35a578f2 --- /dev/null +++ b/docs/source/robosuite.models.robots.manipulators.rst @@ -0,0 +1,77 @@ +robosuite.models.robots.manipulators package +============================================ + +Submodules +---------- + +robosuite.models.robots.manipulators.baxter\_robot module +--------------------------------------------------------- + +.. automodule:: robosuite.models.robots.manipulators.baxter_robot + :members: + :undoc-members: + :show-inheritance: + +robosuite.models.robots.manipulators.iiwa\_robot module +------------------------------------------------------- + +.. automodule:: robosuite.models.robots.manipulators.iiwa_robot + :members: + :undoc-members: + :show-inheritance: + +robosuite.models.robots.manipulators.jaco\_robot module +------------------------------------------------------- + +.. automodule:: robosuite.models.robots.manipulators.jaco_robot + :members: + :undoc-members: + :show-inheritance: + +robosuite.models.robots.manipulators.kinova3\_robot module +---------------------------------------------------------- + +.. automodule:: robosuite.models.robots.manipulators.kinova3_robot + :members: + :undoc-members: + :show-inheritance: + +robosuite.models.robots.manipulators.manipulator\_model module +-------------------------------------------------------------- + +.. automodule:: robosuite.models.robots.manipulators.manipulator_model + :members: + :undoc-members: + :show-inheritance: + +robosuite.models.robots.manipulators.panda\_robot module +-------------------------------------------------------- + +.. automodule:: robosuite.models.robots.manipulators.panda_robot + :members: + :undoc-members: + :show-inheritance: + +robosuite.models.robots.manipulators.sawyer\_robot module +--------------------------------------------------------- + +.. automodule:: robosuite.models.robots.manipulators.sawyer_robot + :members: + :undoc-members: + :show-inheritance: + +robosuite.models.robots.manipulators.ur5e\_robot module +------------------------------------------------------- + +.. automodule:: robosuite.models.robots.manipulators.ur5e_robot + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: robosuite.models.robots.manipulators + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/robosuite.models.robots.rst b/docs/source/robosuite.models.robots.rst index d98b630697..36cba107be 100644 --- a/docs/source/robosuite.models.robots.rst +++ b/docs/source/robosuite.models.robots.rst @@ -1,47 +1,18 @@ -robosuite.models.robots +robosuite.models.robots package =============================== -robosuite.models.robots.baxter\_robot --------------------------------------------- +Subpackages +----------- -.. automodule:: robosuite.models.robots.baxter_robot - :members: - :undoc-members: - :show-inheritance: - -robosuite.models.robots.iiwa\_robot ------------------------------------------- - -.. automodule:: robosuite.models.robots.iiwa_robot - :members: - :undoc-members: - :show-inheritance: +.. toctree:: + :maxdepth: 4 -robosuite.models.robots.jaco\_robot ------------------------------------------- + robosuite.models.robots.manipulators -.. automodule:: robosuite.models.robots.jaco_robot - :members: - :undoc-members: - :show-inheritance: +Submodules +---------- -robosuite.models.robots.kinova3\_robot ---------------------------------------------- - -.. automodule:: robosuite.models.robots.kinova3_robot - :members: - :undoc-members: - :show-inheritance: - -robosuite.models.robots.panda\_robot -------------------------------------------- - -.. automodule:: robosuite.models.robots.panda_robot - :members: - :undoc-members: - :show-inheritance: - -robosuite.models.robots.robot\_model +robosuite.models.robots.robot\_model module ------------------------------------------- .. automodule:: robosuite.models.robots.robot_model @@ -49,18 +20,10 @@ robosuite.models.robots.robot\_model :undoc-members: :show-inheritance: -robosuite.models.robots.sawyer\_robot --------------------------------------------- - -.. automodule:: robosuite.models.robots.sawyer_robot - :members: - :undoc-members: - :show-inheritance: - -robosuite.models.robots.ur5e\_robot ------------------------------------------- +Module contents +--------------- -.. automodule:: robosuite.models.robots.ur5e_robot +.. automodule:: robosuite.models.robots :members: :undoc-members: :show-inheritance: diff --git a/docs/source/robosuite.models.rst b/docs/source/robosuite.models.rst index 38e7ca3be5..d322daa5f1 100644 --- a/docs/source/robosuite.models.rst +++ b/docs/source/robosuite.models.rst @@ -1,16 +1,23 @@ -robosuite.models +robosuite.models package ======================== +Subpackages +----------- + .. toctree:: :maxdepth: 4 robosuite.models.arenas robosuite.models.grippers + robosuite.models.mounts robosuite.models.objects robosuite.models.robots robosuite.models.tasks -robosuite.models.base +Submodules +---------- + +robosuite.models.base module ---------------------------- .. automodule:: robosuite.models.base @@ -18,10 +25,18 @@ robosuite.models.base :undoc-members: :show-inheritance: -robosuite.models.world +robosuite.models.world module ----------------------------- .. automodule:: robosuite.models.world :members: :undoc-members: :show-inheritance: + +Module contents +--------------- + +.. automodule:: robosuite.models + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/robosuite.models.tasks.rst b/docs/source/robosuite.models.tasks.rst index 448b133fe4..e67fba31b5 100644 --- a/docs/source/robosuite.models.tasks.rst +++ b/docs/source/robosuite.models.tasks.rst @@ -1,7 +1,10 @@ -robosuite.models.tasks +robosuite.models.tasks package ============================== -robosuite.models.tasks.manipulation\_task +Submodules +---------- + +robosuite.models.tasks.manipulation\_task module ------------------------------------------------ .. automodule:: robosuite.models.tasks.manipulation_task @@ -9,10 +12,18 @@ robosuite.models.tasks.manipulation\_task :undoc-members: :show-inheritance: -robosuite.models.tasks.placement\_sampler ------------------------------------------------- +robosuite.models.tasks.task module +---------------------------------- + +.. automodule:: robosuite.models.tasks.task + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- -.. automodule:: robosuite.models.tasks.placement_sampler +.. automodule:: robosuite.models.tasks :members: :undoc-members: :show-inheritance: diff --git a/docs/source/robosuite.robots.rst b/docs/source/robosuite.robots.rst index c2f894a5c8..87fb90b4b0 100644 --- a/docs/source/robosuite.robots.rst +++ b/docs/source/robosuite.robots.rst @@ -1,7 +1,10 @@ -robosuite.robots +robosuite.robots package ======================== -robosuite.robots.bimanual +Submodules +---------- + +robosuite.robots.bimanual module -------------------------------- .. automodule:: robosuite.robots.bimanual @@ -9,7 +12,15 @@ robosuite.robots.bimanual :undoc-members: :show-inheritance: -robosuite.robots.robot +robosuite.robots.manipulator module +----------------------------------- + +.. automodule:: robosuite.robots.manipulator + :members: + :undoc-members: + :show-inheritance: + +robosuite.robots.robot module ----------------------------- .. automodule:: robosuite.robots.robot @@ -17,10 +28,18 @@ robosuite.robots.robot :undoc-members: :show-inheritance: -robosuite.robots.single\_arm +robosuite.robots.single\_arm module ----------------------------------- .. automodule:: robosuite.robots.single_arm :members: :undoc-members: :show-inheritance: + +Module contents +--------------- + +.. automodule:: robosuite.robots + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/robosuite.rst b/docs/source/robosuite.rst index 2612771ad0..8f38dc8c86 100644 --- a/docs/source/robosuite.rst +++ b/docs/source/robosuite.rst @@ -1,5 +1,5 @@ robosuite -================= +========= .. toctree:: :maxdepth: 4 diff --git a/docs/source/robosuite.utils.rst b/docs/source/robosuite.utils.rst index d1e31a18f6..5176f18df6 100644 --- a/docs/source/robosuite.utils.rst +++ b/docs/source/robosuite.utils.rst @@ -1,7 +1,18 @@ -robosuite.utils +robosuite.utils package ======================= -robosuite.utils.control\_utils +Submodules +---------- + +robosuite.utils.buffers module +------------------------------ + +.. automodule:: robosuite.utils.buffers + :members: + :undoc-members: + :show-inheritance: + +robosuite.utils.control\_utils module ------------------------------------- .. automodule:: robosuite.utils.control_utils @@ -9,7 +20,7 @@ robosuite.utils.control\_utils :undoc-members: :show-inheritance: -robosuite.utils.errors +robosuite.utils.errors module ----------------------------- .. automodule:: robosuite.utils.errors @@ -17,7 +28,7 @@ robosuite.utils.errors :undoc-members: :show-inheritance: -robosuite.utils.input\_utils +robosuite.utils.input\_utils module ----------------------------------- .. automodule:: robosuite.utils.input_utils @@ -25,7 +36,15 @@ robosuite.utils.input\_utils :undoc-members: :show-inheritance: -robosuite.utils.mjcf\_utils +robosuite.utils.macros module +----------------------------- + +.. automodule:: robosuite.utils.macros + :members: + :undoc-members: + :show-inheritance: + +robosuite.utils.mjcf\_utils module ---------------------------------- .. automodule:: robosuite.utils.mjcf_utils @@ -33,7 +52,7 @@ robosuite.utils.mjcf\_utils :undoc-members: :show-inheritance: -robosuite.utils.mjmod +robosuite.utils.mjmod module ---------------------------- .. automodule:: robosuite.utils.mjmod @@ -41,7 +60,7 @@ robosuite.utils.mjmod :undoc-members: :show-inheritance: -robosuite.utils.mujoco\_py\_renderer +robosuite.utils.mujoco\_py\_renderer module ------------------------------------------- .. automodule:: robosuite.utils.mujoco_py_renderer @@ -49,7 +68,7 @@ robosuite.utils.mujoco\_py\_renderer :undoc-members: :show-inheritance: -robosuite.utils.numba +robosuite.utils.numba module ---------------------------- .. automodule:: robosuite.utils.numba @@ -57,10 +76,42 @@ robosuite.utils.numba :undoc-members: :show-inheritance: -robosuite.utils.transform\_utils +robosuite.utils.observables module +---------------------------------- + +.. automodule:: robosuite.utils.observables + :members: + :undoc-members: + :show-inheritance: + +robosuite.utils.placement\_samplers module +------------------------------------------ + +.. automodule:: robosuite.utils.placement_samplers + :members: + :undoc-members: + :show-inheritance: + +robosuite.utils.robot\_utils module +----------------------------------- + +.. automodule:: robosuite.utils.robot_utils + :members: + :undoc-members: + :show-inheritance: + +robosuite.utils.transform\_utils module --------------------------------------- .. automodule:: robosuite.utils.transform_utils :members: :undoc-members: :show-inheritance: + +Module contents +--------------- + +.. automodule:: robosuite.utils + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/robosuite.wrappers.rst b/docs/source/robosuite.wrappers.rst index 33ce1a16ce..39839d7d4b 100644 --- a/docs/source/robosuite.wrappers.rst +++ b/docs/source/robosuite.wrappers.rst @@ -1,6 +1,9 @@ -robosuite.wrappers +robosuite.wrappers package ========================== +Submodules +---------- + robosuite.wrappers.data\_collection\_wrapper module --------------------------------------------------- @@ -33,6 +36,14 @@ robosuite.wrappers.gym\_wrapper module :undoc-members: :show-inheritance: +robosuite.wrappers.visualization\_wrapper module +------------------------------------------------ + +.. automodule:: robosuite.wrappers.visualization_wrapper + :members: + :undoc-members: + :show-inheritance: + robosuite.wrappers.wrapper module --------------------------------- @@ -40,3 +51,11 @@ robosuite.wrappers.wrapper module :members: :undoc-members: :show-inheritance: + +Module contents +--------------- + +.. automodule:: robosuite.wrappers + :members: + :undoc-members: + :show-inheritance: From 90ea788f1f2d2da667a9de512e0c393b36c60142 Mon Sep 17 00:00:00 2001 From: Yuke Zhu Date: Thu, 4 Feb 2021 21:02:10 -0600 Subject: [PATCH 06/16] add new references and fix rst style issue --- docs/modeling/arena.rst | 4 ++-- docs/modeling/mujoco_model.rst | 2 +- docs/modeling/object_model.rst | 12 ++++++------ docs/modeling/robot_model.rst | 10 +++++----- docs/modeling/task.rst | 5 ++--- docs/modules/overview.md | 2 +- docs/modules/sensors.md | 6 +++--- docs/overview.md | 2 +- docs/references.md | 3 +++ docs/simulation/controller.rst | 2 +- docs/simulation/device.rst | 4 ++-- docs/simulation/environment.rst | 6 +++--- docs/simulation/robot.rst | 2 +- 13 files changed, 31 insertions(+), 29 deletions(-) diff --git a/docs/modeling/arena.rst b/docs/modeling/arena.rst index e0c890a215..9116598d11 100644 --- a/docs/modeling/arena.rst +++ b/docs/modeling/arena.rst @@ -1,9 +1,9 @@ Arena ===== -The `Arena` class serves as a base model for building the simulation environment. +The ``Arena`` class serves as a base model for building the simulation environment. By default, this includes a ground plane and visual walls, and child classes extend this -to additionally include other objects, e.g.: a table or bins. +to additionally include other objects, e.g., a table or bins. Base Arena ---------- diff --git a/docs/modeling/mujoco_model.rst b/docs/modeling/mujoco_model.rst index d496120124..f164ac83a1 100644 --- a/docs/modeling/mujoco_model.rst +++ b/docs/modeling/mujoco_model.rst @@ -1,7 +1,7 @@ Mujoco Model ============ -The `MujocoModel` class is the foundational class from which all other model classes extend from in robosuite. This class represents a standardized API for all models used in simulation and is the core modeling component that other model classes build upon. The `MujocoXMLModel` is an extension of this class that represents models based on an XML file. +The ``MujocoModel`` class is the foundational class from which all other model classes extend from in robosuite. This class represents a standardized API for all models used in simulation and is the core modeling component that other model classes build upon. The ``MujocoXMLModel`` is an extension of this class that represents models based on an XML file. Base Mujoco Model ----------------- diff --git a/docs/modeling/object_model.rst b/docs/modeling/object_model.rst index ef6fdb4a25..12c8a46596 100644 --- a/docs/modeling/object_model.rst +++ b/docs/modeling/object_model.rst @@ -1,12 +1,12 @@ Object Model ============ -The `MujocoObject` class serves as a catch-all base class that is used to capture individual simulation objects to -instantiate within a given simulation. This is done in one of two ways via extended classes -- the `MujocoXMLObject` -reads in information from a corresponding object XML file, whereas the `MujocoGeneratedObject` proecedurally generates a -custom object using a suite of utility mj modeling functions. In conjunction with the `RobotModel`, and -`Arena` classes, these classes serve as the basis for forming the higher level `Task` class which is used to -ultimately generate the `MjSim` simulation object. +The ``MujocoObject`` class serves as a catch-all base class that is used to capture individual simulation objects to +instantiate within a given simulation. This is done in one of two ways via extended classes -- the ``MujocoXMLObject`` +reads in information from a corresponding object XML file, whereas the ``MujocoGeneratedObject`` proecedurally generates a +custom object using a suite of utility mj modeling functions. In conjunction with the ``RobotModel``, and +``Arena`` classes, these classes serve as the basis for forming the higher level ``Task`` class which is used to +ultimately generate the ``MjSim`` simulation object. Base Object Model ----------------- diff --git a/docs/modeling/robot_model.rst b/docs/modeling/robot_model.rst index d3b3288cec..ac8a4084ef 100644 --- a/docs/modeling/robot_model.rst +++ b/docs/modeling/robot_model.rst @@ -3,8 +3,8 @@ Robot Model Robot Model ----------- -The `RobotModel` class serves as a direct intermediary class that reads in information from a corresponding robot XML -file and also contains relevant hard-coded information from that XML. This represents an arbitrary robot optionally equipped with a mount via the `MountModel` class and is the core modeling component of the higher-level `Robot` class used in simulation. +The ``RobotModel`` class serves as a direct intermediary class that reads in information from a corresponding robot XML +file and also contains relevant hard-coded information from that XML. This represents an arbitrary robot optionally equipped with a mount via the ``MountModel`` class and is the core modeling component of the higher-level ``Robot`` class used in simulation. .. autoclass:: robosuite.models.robots.robot_model.RobotModel @@ -25,7 +25,7 @@ file and also contains relevant hard-coded information from that XML. This repre Manipulator Model ----------------- -The `ManipulatorModel` class extends from the base `RobotModel` class, and represents an armed, mounted robot with an optional gripper attached to its end effector. In conjunction with the corresponding `GripperModel` class and `MountModel` class, this serves as the core modeling component of the higher-level `Manipulator` class used in simulation. +The ``ManipulatorModel`` class extends from the base ``RobotModel`` class, and represents an armed, mounted robot with an optional gripper attached to its end effector. In conjunction with the corresponding ``GripperModel`` class and ``MountModel`` class, this serves as the core modeling component of the higher-level ``Manipulator`` class used in simulation. .. autoclass:: robosuite.models.robots.manipulators.manipulator_model.ManipulatorModel @@ -39,7 +39,7 @@ The `ManipulatorModel` class extends from the base `RobotModel` class, and repre Gripper Model ------------- -The `GripperModel` class serves as a direct intermediary class that reads in information from a corresponding gripper XML file and also contains relevant hard-coded information from that XML. In conjunction with the `ManipulatorModel` class, this serves as the core modeling component of the higher-level `Manipulator` class used in simulation. +The ``GripperModel`` class serves as a direct intermediary class that reads in information from a corresponding gripper XML file and also contains relevant hard-coded information from that XML. In conjunction with the ``ManipulatorModel`` class, this serves as the core modeling component of the higher-level `Manipulator` class used in simulation. .. autoclass:: robosuite.models.grippers.gripper_model.GripperModel @@ -54,7 +54,7 @@ The `GripperModel` class serves as a direct intermediary class that reads in inf Mount Model ----------- -The `MountModel` class serves as a direct intermediary class that reads in information from a corresponding mount XML file and also contains relevant hard-coded information from that XML. In conjunction with the `RobotModel` class, this serves as the core modeling component of the higher-level Robot class used in simulation. +The ``MountModel`` class serves as a direct intermediary class that reads in information from a corresponding mount XML file and also contains relevant hard-coded information from that XML. In conjunction with the ``RobotModel`` class, this serves as the core modeling component of the higher-level Robot class used in simulation. .. autoclass:: robosuite.models.mounts.mount_model.MountModel diff --git a/docs/modeling/task.rst b/docs/modeling/task.rst index 3bb1adc294..5e7338ae6a 100644 --- a/docs/modeling/task.rst +++ b/docs/modeling/task.rst @@ -1,11 +1,10 @@ Task ===== -The `Task` class is responsible for integrating a given `Arena`, `RobotModel`, and set of `MujocoObjects` into a single element -tree that is then parsed and converted into an `MjSim` object. +The ``Task`` class is responsible for integrating a given ``Arena``, ``RobotModel``, and set of ``MujocoObjects`` into a single element tree that is then parsed and converted into an ``MjSim`` object. Base Task ------------------ +--------- .. autoclass:: robosuite.models.tasks.task.Task diff --git a/docs/modules/overview.md b/docs/modules/overview.md index 9f514bccad..52eafc5a40 100644 --- a/docs/modules/overview.md +++ b/docs/modules/overview.md @@ -4,7 +4,7 @@ Our framework offers two main categories of APIs: 1) **Modeling APIs** for defining simulation environments in a modular and programmatic fashion, and 2) **Simulation APIs** for interfacing with external inputs such as from a **Policy** or an **I/O Device**. A **Simulation Model** specified by the Modeling APIs is instantiated by the **MuJoCo Engine** to create a simulation runtime, called **Environment**. The Environment generates observations through the **Sensors**, such as cameras and proprioception, and receives action commands from policies or devices through the **Controllers** of the **Robots**. The diagram above illustrates the key components in our framework and their relationships. -A simulation model is defined by a [Task](../modeling/task) object, which encapsulates three essential constituents of robotic simulation: [Robot Model](../modeling/robot_model)s, [Object Model](../modeling/object_model)s, and [Arena](../modeling/arena). A task may contain one or more robots, zero to many objects, and a single arena. The Robot Model loads models of robots and optionally other models as well; for example, the [Manipulator](../) robot model class also loads a corresponding [Gripper Model](../modeling/gripper_model)s from XML files. The Object Model can be either loaded from 3D object assets or procedurally generated with programmatic APIs. The Arena defines the workspace of the robot, including the environment fixtures, such as a tabletop, and their placements. The task class combines these constituents into a single XML object in MuJoCo's [MJCF modeling language](http://www.mujoco.org/book/XMLreference.html). This MJCF object is passed to the MuJoCo engine through the [mujoco-py](https://openai.github.io/mujoco-py/build/html/index.html) library to instantiate the [MjSim](https://openai.github.io/mujoco-py/build/html/reference.html#mjsim-basic-simulation) object for simulation runtime. +A simulation model is defined by a [Task](../modeling/task) object, which encapsulates three essential constituents of robotic simulation: [Robot Model](../modeling/robot_model)s, [Object Model](../modeling/object_model)s, and [Arena](../modeling/arena). A task may contain one or more robots, zero to many objects, and a single arena. The Robot Model loads models of robots and optionally other models as well; for example, the [Manipulator](../modeling/robot_model.html#manipulator-model) robot model class also loads a corresponding [Gripper Model](../modeling/robot_model.html#gripper-model)s from XML files. The Object Model can be either loaded from 3D object assets or procedurally generated with programmatic APIs. The Arena defines the workspace of the robot, including the environment fixtures, such as a tabletop, and their placements. The task class combines these constituents into a single XML object in MuJoCo's [MJCF modeling language](http://www.mujoco.org/book/XMLreference.html). This MJCF object is passed to the MuJoCo engine through the [mujoco-py](https://openai.github.io/mujoco-py/build/html/index.html) library to instantiate the [MjSim](https://openai.github.io/mujoco-py/build/html/reference.html#mjsim-basic-simulation) object for simulation runtime. The [Environment](environments) object provides [OpenAI Gym](https://gym.openai.com/)-style APIs for external inputs to interface with the simulation. External inputs correspond to the action commands used to control the [Robots](robots) and any actuators it owns (for example, in the case of a manipulator robot the arm joints and gripper), where the kinematic component of the action spaces are specific to the [Controllers](controllers) used by the robots. For instance, for joint-space controllers of a robot manipulator arm, the action space corresponds to the number of joints of the robot, and for operational space controllers, the action space corresponds to 3D Cartesian movement or 6D pose of the end-effector. These action commands can either be automatically generated by an algorithm (such as a deep neural network policy) or come from [I/O devices](devices) for human teleoperation (such as the keyboard). The controllers of the robots are responsible for interpreting these action commands and transforming them into the low-level torques passing to the underlying physics engine, which performs internal computations to determine the next state of the simulation. [Sensors](./sensors) retrieve information from the MjSim object and generate observations as the physical signals that the robots receive as response to their actions. Our framework supports multimodal sensing modalities, such as RGB-D cameras, force-torque measurements, and proprioceptive data, and also provides modular APIs to model realistic sensor dynamics. In addition to these sensory data, environments also provide additional information about the task progress and success conditions, including reward functions (for reinforcement learning) and other meta-data. For more information, please check out the descriptions of the individual components in this section. diff --git a/docs/modules/sensors.md b/docs/modules/sensors.md index c94ec814df..c5d83b944c 100644 --- a/docs/modules/sensors.md +++ b/docs/modules/sensors.md @@ -4,7 +4,7 @@ Sensors are an important aspect of **robosuite**, and encompass an agent's feedb #### Mujoco-Native Sensors -The simulator generates virtual physical signals as response to a robot's interactions. Virtual signals include images, force-torque measurements (from a force-torque sensor like the one included by default in the wrist of all [Gripper models](../modeling/gripper_model)), pressure signals (e.g. from a sensor on the robot's finger or on the environment), etc. Raw sensor information (except cameras and joint sensors) can be accessed via the function `get_sensor_measurement` provided the name of the sensor. +The simulator generates virtual physical signals as response to a robot's interactions. Virtual signals include images, force-torque measurements (from a force-torque sensor like the one included by default in the wrist of all [Gripper models](../modeling/robot_model.html#gripper-model)), pressure signals (e.g. from a sensor on the robot's finger or on the environment), etc. Raw sensor information (except cameras and joint sensors) can be accessed via the function `get_sensor_measurement` provided the name of the sensor. Joint sensors provide information about the state of each robot's joint including position and velocity. In MuJoCo these are not measured by sensors, but resolved and set by the simulator as the result of the actuation forces. Therefore, they are not accessed through the common `get_sensor_measurement` function but as properties of the [Robot simulation API](../simulation/robot), i.e., `_joint_positions` and `_joint_velocities`. @@ -12,7 +12,7 @@ Cameras bundle a name to a set of properties to render images of the environment #### Observables -**robosuite** provides a realistic, customizable interface via the [Observable](../../../robosuite/utils/observables.py) class API. Observables model realistic sensor sampling, in which ground truth data is sampled (`sensor`), passed through a corrupting function (`corrupter`), and then finally passed through a filtering function (`filter`). Moreover, each observable has its own `sampling_rate` and `delayer` function which simulates sensor delay. While default values are used to instantiate each observable during environment creation, each of these components can be modified by the user at runtime using `env.modify_observable(...)` . Moreover, each observable is assigned a modality, and are grouped together in the returned observation dictionary during the `env.step()` call. For example, if an environment consists of camera observations and a single robot's proprioceptive observations, the observation dict structure might look as follows: +**robosuite** provides a realistic, customizable interface via the [Observable](../source/robosuite.utils.html#module-robosuite.utils.observables) class API. Observables model realistic sensor sampling, in which ground truth data is sampled (`sensor`), passed through a corrupting function (`corrupter`), and then finally passed through a filtering function (`filter`). Moreover, each observable has its own `sampling_rate` and `delayer` function which simulates sensor delay. While default values are used to instantiate each observable during environment creation, each of these components can be modified by the user at runtime using `env.modify_observable(...)` . Moreover, each observable is assigned a modality, and are grouped together in the returned observation dictionary during the `env.step()` call. For example, if an environment consists of camera observations and a single robot's proprioceptive observations, the observation dict structure might look as follows: ```python { @@ -27,7 +27,7 @@ Cameras bundle a name to a set of properties to render images of the environment Note that for memory efficiency the `image-state` is not returned by default (this can be toggled in `robosuite/utils/macros.py`). -We showcase how the `Observable` functionality can be used to model sensor corruption and delay via [demo_sensor_corruption.py](../../../robosuite/demos/demo_sensor_corruption.py). We also highlight that each of the `sensor`, `corrupter`, and `filter` functions can be arbitrarily specified to suit the end-user's usage. For example, a common use case for these observables is to keep track of sampled values from a sensor operating at a higher frequency than the environment step (control) frequency. In this case, the `filter` function can be leveraged to keep track of the real-time sensor values as they're being sampled. We provide a minimal script showcasing this ability below: +We showcase how the `Observable` functionality can be used to model sensor corruption and delay via [demo_sensor_corruption.py](../demos.html#sensor-realism). We also highlight that each of the `sensor`, `corrupter`, and `filter` functions can be arbitrarily specified to suit the end-user's usage. For example, a common use case for these observables is to keep track of sampled values from a sensor operating at a higher frequency than the environment step (control) frequency. In this case, the `filter` function can be leveraged to keep track of the real-time sensor values as they're being sampled. We provide a minimal script showcasing this ability below: ```python import robosuite as suite diff --git a/docs/overview.md b/docs/overview.md index eeea6cf596..2dac9950b6 100644 --- a/docs/overview.md +++ b/docs/overview.md @@ -10,7 +10,7 @@ Data-driven algorithms, such as reinforcement learning and imitation learning, p * a modular design that offers great flexibility to design new robot simulation environments; * a high-quality implementation of robot controllers and off-the-shelf learning algorithms to lower the barriers to entry. -This framework was originally developed since late 2017 by researchers in [Stanford Vision and Learning Lab](http://svl.stanford.edu/) (SVL) as an internal tool for robot learning research. Now it is actively maintained and used for robotics research projects in SVL and the [UT-Austin Robot Perception and Learning (RPL) Lab](http://rpl.cs.utexas.edu). We welcome community contributions to this project. For details please check out our [contributing guidelines](https://github.com/ARISE-Initiative/robosuite/blob/master/CONTRIBUTING.md). +This framework was originally developed since late 2017 by researchers in [Stanford Vision and Learning Lab](http://svl.stanford.edu/) (SVL) as an internal tool for robot learning research. Now it is actively maintained and used for robotics research projects in SVL and the [UT-Austin Robot Perception and Learning (RPL) Lab](http://rpl.cs.utexas.edu). We welcome community contributions to this project. For details please check out our [contributing guidelines](https://github.com/ARISE-Initiative/robosuite/blob/master/CONTRIBUTING.md#). This release of **robosuite** contains seven robot models, eight gripper models, six controller modes, and nine standardized tasks. It also offers a modular design of APIs for building new environments with procedural generation. We highlight these primary features below: diff --git a/docs/references.md b/docs/references.md index dddfc53fd7..57e1ef4ba7 100644 --- a/docs/references.md +++ b/docs/references.md @@ -15,6 +15,7 @@ A list of references of projects and papers that use **robosuite**. If you would - [Hierarchical 6-DoF Grasping with Approaching Direction Selection](http://rllab.snu.ac.kr/publications/papers/2020_icra_gads.pdf). Yunho Choi, Hogun Kee, Kyungjae Lee, JaeGoo Choy, Junhong Min, Sohee Lee, and Songhwai Oh - [Conservative Safety Critics for Exploration](https://arxiv.org/abs/2010.14497). Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, Animesh Garg - [Deep Reinforcement Learning for Contact-Rich Skills Using Compliant Movement Primitives](https://arxiv.org/abs/2008.13223). Oren Spector, Miriam Zacksenhouse +- [Continual Model-Based Reinforcement Learning with Hypernetworks](https://arxiv.org/abs/2009.11997). Yizhou Huang, Kevin Xie, Homanga Bharadhwaj, Florian Shkurti ## Imitation Learning and Batch (Offline) Reinforcement Learning @@ -25,6 +26,8 @@ A list of references of projects and papers that use **robosuite**. If you would - [Residual Learning from Demonstration](https://arxiv.org/abs/2008.07682). Todor Davchev, Kevin Sebastian Luck, Michael Burke, Franziska Meier, Stefan Schaal, Subramanian Ramamoorthy - [Variational Imitation Learning with Diverse-quality Demonstrations](https://proceedings.icml.cc/static/paper_files/icml/2020/577-Paper.pdf). Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama - [Transformers for One-Shot Visual Imitation](https://corlconf.github.io/paper_463/). Sudeep Dasari, Abhinav Gupta +- [Learning Multi-Arm Manipulation Through Collaborative Teleoperation](https://arxiv.org/abs/2012.06738) Albert Tung, Josiah Wong, Ajay Mandlekar, Roberto Martín-Martín, Yuke Zhu, Li Fei-Fei, Silvio Savarese +- [Human-in-the-Loop Imitation Learning using Remote Teleoperation](https://arxiv.org/abs/2012.06733) Ajay Mandlekar, Danfei Xu, Roberto Martín-Martín, Yuke Zhu, Li Fei-Fei, Silvio Savarese ## Benchmarks diff --git a/docs/simulation/controller.rst b/docs/simulation/controller.rst index e796dea947..1ff5856c62 100644 --- a/docs/simulation/controller.rst +++ b/docs/simulation/controller.rst @@ -1,7 +1,7 @@ Controller ========== -Every Robot is equipped with a controller, which determines both the action space as well as how its +Every ``Robot`` is equipped with a controller, which determines both the action space as well as how its values are mapped into command torques. By default, all controllers have a pre-defined set of methods and properities, though specific controllers may extend and / or override the default functionality found in the base class. diff --git a/docs/simulation/device.rst b/docs/simulation/device.rst index adb8bd736a..216b70b160 100644 --- a/docs/simulation/device.rst +++ b/docs/simulation/device.rst @@ -1,7 +1,7 @@ Device ====== -Devices allow for direct real-time interfacing with the MuJoCo simulation. The current support devices are Keyboard and Spacemouse. +Devices allow for direct real-time interfacing with the MuJoCo simulation. The current support devices are ``Keyboard`` and ``SpaceMouse``. Base Device ----------- @@ -23,7 +23,7 @@ Keyboard Device .. automethod:: _display_controls -Spacemouse Device +SpaceMouse Device ----------------- .. autoclass:: robosuite.devices.spacemouse.SpaceMouse diff --git a/docs/simulation/environment.rst b/docs/simulation/environment.rst index 767c3f4212..f35a89f967 100644 --- a/docs/simulation/environment.rst +++ b/docs/simulation/environment.rst @@ -1,9 +1,9 @@ Environment =========== -The MujocoEnv class defines a top-level simulation object encapsulating a MjSim object. Robosuite uses -class extensions of this base class, namely, RobotEnv which additionally encompasses Robot objects and the top-level -task environments which capture specific ManipulationTask instances and additional objects. +The ``MujocoEnv`` class defines a top-level simulation object encapsulating a ``MjSim`` object. Robosuite uses +class extensions of this base class, namely, ``RobotEnv`` which additionally encompasses ``Robot`` objects and the top-level +task environments which capture specific ``ManipulationTask`` instances and additional objects. Base Environment ---------------- diff --git a/docs/simulation/robot.rst b/docs/simulation/robot.rst index 2b819b0ba3..9b9673c2ad 100644 --- a/docs/simulation/robot.rst +++ b/docs/simulation/robot.rst @@ -1,7 +1,7 @@ Robot ===== -The `Robot` class defines a simulation object encapsulating a robot model, gripper model, and controller. Robosuite uses class extensions of this base class to model different robotic domains. The current release focuses on manipulation, and includes a `Manipulator` class, which itself is extended by `SingleArm` and `Bimanual` classes representing the two different types of supported robots. +The ``Robot`` class defines a simulation object encapsulating a robot model, gripper model, and controller. Robosuite uses class extensions of this base class to model different robotic domains. The current release focuses on manipulation, and includes a ``Manipulator`` class, which itself is extended by ``SingleArm`` and ``Bimanual`` classes representing the two different types of supported robots. Base Robot ---------- From 1070a4580aa5a96082e94fec6e7db54d6a67383f Mon Sep 17 00:00:00 2001 From: Yuke Zhu Date: Thu, 4 Feb 2021 21:17:25 -0600 Subject: [PATCH 07/16] fix broken links --- docs/algorithms/demonstrations.md | 8 ++++---- docs/modules/robots.md | 2 +- docs/quickstart.md | 2 +- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/algorithms/demonstrations.md b/docs/algorithms/demonstrations.md index 403e2de29a..4beb0234e6 100644 --- a/docs/algorithms/demonstrations.md +++ b/docs/algorithms/demonstrations.md @@ -2,7 +2,7 @@ ## Collecting Human Demonstrations -We provide teleoperation utilities that allow users to control the robots with input devices, such as the keyboard and the [SpaceMouse](https://www.3dconnexion.com/spacemouse_compact/en/). Such functionality allows us to collect a dataset of human demonstrations for learning. We provide an example script to illustrate how to collect demonstrations. Our [collect_human_demonstrations](robosuite/scripts/collect_human_demonstrations.py) script takes the following arguments: +We provide teleoperation utilities that allow users to control the robots with input devices, such as the keyboard and the [SpaceMouse](https://www.3dconnexion.com/spacemouse_compact/en/). Such functionality allows us to collect a dataset of human demonstrations for learning. We provide an example script to illustrate how to collect demonstrations. Our [collect_human_demonstrations](https://github.com/ARISE-Initiative/robosuite/blob/master/robosuite/scripts/collect_human_demonstrations.py) script takes the following arguments: - `directory:` path to a folder for where to store the pickle file of collected demonstrations - `environment:` name of the environment you would like to collect the demonstrations for @@ -38,14 +38,14 @@ Note that the rendering window must be active for these commands to work. ## Replaying Human Demonstrations -We have included an example script that illustrates how demonstrations can be loaded and played back. Our [playback_demonstrations_from_hdf5](robosuite/scripts/playback_demonstrations_from_hdf5.py) script selects demonstration episodes at random from a demonstration pickle file and replays them. +We have included an example script that illustrates how demonstrations can be loaded and played back. Our [playback_demonstrations_from_hdf5](https://github.com/ARISE-Initiative/robosuite/blob/master/robosuite/scripts/playback_demonstrations_from_hdf5.py) script selects demonstration episodes at random from a demonstration pickle file and replays them. ## Existing Datasets We have included some sample demonstrations for each task at `models/assets/demonstrations`. -Our twin project [RoboTurk](http://roboturk.stanford.edu) has also collected pilot datasets of more than a thousand demonstrations for two tasks in our suite via crowdsourcing. You can find detailed information about the RoboTurk datasets [here](docs/demonstrations.md#roboturk-dataset). +Our twin project [RoboTurk](http://roboturk.stanford.edu) has also collected pilot datasets of more than a thousand demonstrations for two tasks in our suite via crowdsourcing. You can find detailed information about the RoboTurk datasets [here](roboturk). ## Structure of collected demonstrations @@ -81,4 +81,4 @@ The reason for storing mujoco states instead of raw observations is to make it e ## Using Demonstrations for Learning -[Several](https://arxiv.org/abs/1802.09564) [prior](https://arxiv.org/abs/1807.06919) [works](https://arxiv.org/abs/1804.02717) have demonstrated the effectiveness of altering the start state distribution of training episodes for learning RL policies. We provide a generic utility for setting various types of learning curriculums which dictate how to sample from demonstration episodes when doing an environment reset. For more information see the `DemoSamplerWrapper` class. We have provided an example of how to use this wrapper along with a demonstration pickle file in the [demo_learning_curriculum](robosuite/scripts/demo_learning_curriculum.py) script. \ No newline at end of file +[Several](https://arxiv.org/abs/1802.09564) [prior](https://arxiv.org/abs/1807.06919) [works](https://arxiv.org/abs/1804.02717) have demonstrated the effectiveness of altering the start state distribution of training episodes for learning RL policies. We provide a generic utility for setting various types of learning curriculums which dictate how to sample from demonstration episodes when doing an environment reset. For more information see the `DemoSamplerWrapper` class. \ No newline at end of file diff --git a/docs/modules/robots.md b/docs/modules/robots.md index 11f237256a..b0e545cd1c 100644 --- a/docs/modules/robots.md +++ b/docs/modules/robots.md @@ -27,7 +27,7 @@ At any given time, each robot has a set of `properties` whose real-time values c ## Models -**robosuite** is designed to be generalizable to multiple robotic domains. The current release focuses on manipulator robots. +**robosuite** is designed to be generalizable to multiple robotic domains. The current release focuses on manipulator robots. For adding new robots, we provide a [rudimentary guide](https://docs.google.com/document/d/1bSUKkpjmbKqWyV5Oc7_4VL4FGKAQZx8aWm_nvlmTVmE/edit?usp=sharing) on how to import raw Robot and Gripper models (based on a URDF source file) into robosuite. ### Manipulators **robosuite** currently supports seven commercially-available manipulator robot models. We briefly describe each individual model along with its features below: diff --git a/docs/quickstart.md b/docs/quickstart.md index 713ff778f5..decf4d3dc0 100644 --- a/docs/quickstart.md +++ b/docs/quickstart.md @@ -32,7 +32,7 @@ Many other parameters can be configured for each environment. They provide funct Demo scripts that showcase various features of **robosuite** are available [here](demos). The purpose of each script and usage instructions can be found at the beginning of each file. ## Building Your Own Environments -**robosuite** offers great flexibility in creating your own environments. A [task](modeling/task) typically involves the participation of a [robot](modeling/robot_model) with [grippers](modeling/gripper_model) as its end-effectors, an [arena](modeling/arena) (workspace), and [objects](modeling/object_model) that the robot interacts with. For a detailed overview of our design architecture, please check out the [Overview](modules/overview) page in Modules. Our Modeling APIs provide methods of composing these modularized elements into a scene, which can be loaded in MuJoCo for simulation. To build your own environments, we recommend you take a look at the [Environment classes](simulation/environment) which have used these APIs to define robotics environments and tasks and the [source code](https://github.com/Unknown-Initiative/robosuite-dev/tree/master/robosuite/environments) of our standardized environments. Below we walk through a step-by-step example of building a new tabletop manipulation environment with our APIs. +**robosuite** offers great flexibility in creating your own environments. A [task](modeling/task) typically involves the participation of a [robot](modeling/robot_model) with [grippers](modeling/robot_model.html#gripper-model) as its end-effectors, an [arena](modeling/arena) (workspace), and [objects](modeling/object_model) that the robot interacts with. For a detailed overview of our design architecture, please check out the [Overview](modules/overview) page in Modules. Our Modeling APIs provide methods of composing these modularized elements into a scene, which can be loaded in MuJoCo for simulation. To build your own environments, we recommend you take a look at the [Environment classes](simulation/environment) which have used these APIs to define robotics environments and tasks and the [source code](https://github.com/ARISE-Initiative/robosuite/tree/master/robosuite/environments) of our standardized environments. Below we walk through a step-by-step example of building a new tabletop manipulation environment with our APIs. **Step 1: Creating the world.** All mujoco object definitions are housed in an xml. We create a [MujocoWorldBase](source/robosuite.models) class to do it. ```python From d864858404ab79b5f2d0dfd50f270c1efc939361 Mon Sep 17 00:00:00 2001 From: Yuke Zhu Date: Thu, 4 Feb 2021 21:48:40 -0600 Subject: [PATCH 08/16] update apidoc command --- docs/Makefile | 3 +++ docs/source/robosuite.rst | 15 +++++++++++++-- 2 files changed, 16 insertions(+), 2 deletions(-) diff --git a/docs/Makefile b/docs/Makefile index 3cd236b15e..5e3fc7487d 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -12,6 +12,9 @@ BUILDDIR = _build help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) +apidoc: + @sphinx-apidoc -T --force ../robosuite -o source + .PHONY: help Makefile # Catch-all target: route all unknown targets to Sphinx using the new diff --git a/docs/source/robosuite.rst b/docs/source/robosuite.rst index 8f38dc8c86..fd4c8b177c 100644 --- a/docs/source/robosuite.rst +++ b/docs/source/robosuite.rst @@ -1,5 +1,8 @@ -robosuite -========= +robosuite package +================= + +Subpackages +----------- .. toctree:: :maxdepth: 4 @@ -11,3 +14,11 @@ robosuite robosuite.robots robosuite.utils robosuite.wrappers + +Module contents +--------------- + +.. automodule:: robosuite + :members: + :undoc-members: + :show-inheritance: From e5db425c412078fe262b5e2c10b41695aac663bd Mon Sep 17 00:00:00 2001 From: Yuke Zhu Date: Thu, 11 Feb 2021 20:47:13 -0600 Subject: [PATCH 09/16] update module_overview.png with renderers --- docs/images/module_overview.png | Bin 47055 -> 54626 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/docs/images/module_overview.png b/docs/images/module_overview.png index 9393a0076fd1c784b4869d5828f87b41c9ec5d8f..d31460f3b6ba8e0f61982f4a08c02912a589a4cb 100644 GIT binary patch literal 54626 zcmZsD1z43!*EVd?1E?UafOL0vclV~dySqW9kq$vZnoW0yq%_jqA>Cd7gPwC<-uL@; zUl+5Vm{>Dw&ARWkW(Uj3h`xr$golEHdMz#{qyPo=tN;oM+8_1>aOK4U7`Olh?Vunk z098DQy#;)cGg1{dmX?O10LrjXAZTQ$XOE8n|DgiSpkRKNp`b{Bzd%*#(Equc4*KWr 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z!xpUnu*t;0B2b9fen$yQ+V)HODt_zAA%Oo6N*4=v^J^Kg{RWQh;{SI&h Date: Sat, 13 Feb 2021 11:01:18 -0800 Subject: [PATCH 11/16] add sim2real section, partition relevant sensor info in docs --- docs/index.rst | 8 +++ docs/modules/sensors.md | 52 +----------------- docs/sim2real/dynamics_randomization.md | 71 +++++++++++++++++++++++++ docs/sim2real/sensor_randomization.md | 68 +++++++++++++++++++++++ docs/sim2real/visual_randomization.md | 9 ++++ 5 files changed, 157 insertions(+), 51 deletions(-) create mode 100644 docs/sim2real/dynamics_randomization.md create mode 100644 docs/sim2real/sensor_randomization.md create mode 100644 docs/sim2real/visual_randomization.md diff --git a/docs/index.rst b/docs/index.rst index 4a0ea8d1b8..050051a115 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -61,6 +61,14 @@ Welcome to robosuite's documentation! algorithms/demonstrations algorithms/roboturk +.. toctree:: + :maxdepth: 1 + :caption: Sim2Real + + sim2real/sensor_randomization + sim2real/visual_randomization + sim2real/dynamics_randomization + .. toctree:: :maxdepth: 1 :caption: Miscellaneous diff --git a/docs/modules/sensors.md b/docs/modules/sensors.md index c5d83b944c..c0bd88e0b8 100644 --- a/docs/modules/sensors.md +++ b/docs/modules/sensors.md @@ -27,54 +27,4 @@ Cameras bundle a name to a set of properties to render images of the environment Note that for memory efficiency the `image-state` is not returned by default (this can be toggled in `robosuite/utils/macros.py`). -We showcase how the `Observable` functionality can be used to model sensor corruption and delay via [demo_sensor_corruption.py](../demos.html#sensor-realism). We also highlight that each of the `sensor`, `corrupter`, and `filter` functions can be arbitrarily specified to suit the end-user's usage. For example, a common use case for these observables is to keep track of sampled values from a sensor operating at a higher frequency than the environment step (control) frequency. In this case, the `filter` function can be leveraged to keep track of the real-time sensor values as they're being sampled. We provide a minimal script showcasing this ability below: - -```python -import robosuite as suite -import numpy as np -from robosuite.utils.buffers import RingBuffer - -# Create env instance -control_freq = 10 -env = suite.make("Lift", robots="Panda", has_offscreen_renderer=False, use_camera_obs=False, control_freq=control_freq) - -# Define a ringbuffer to store joint position values -buffer = RingBuffer(dim=env.robots[0].robot_model.dof, length=10) - -# Create a function that we'll use as the "filter" for the joint position Observable -# This is a pass-through operation, but we record the value every time it gets called -# As per the Observables API, this should take in an arbitrary numeric and return the same type / shape -def filter_fcn(corrupted_value): - # Record the inputted value - buffer.push(corrupted_value) - # Return this value (no-op performed) - return corrupted_value - -# Now, let's enable the joint position Observable with this filter function -env.modify_observable( - observable_name="robot0_joint_pos", - attribute="filter", - modifier=filter_fcn, -) - -# Let's also increase the sampling rate to showcase the Observable's ability to update multiple times per env step -obs_sampling_freq = control_freq * 4 -env.modify_observable( - observable_name="robot0_joint_pos", - attribute="sampling_rate", - modifier=obs_sampling_freq, -) - -# Take a single environment step with positive joint velocity actions -arm_action = np.ones(env.robots[0].robot_model.dof) * 1.0 -gripper_action = [1] -action = np.concatenate([arm_action, gripper_action]) -env.step(action) - -# Now we can analyze what values were recorded -np.set_printoptions(precision=2) -print(f"\nPolicy Frequency: {control_freq}, Observable Sampling Frequency: {obs_sampling_freq}") -print(f"Number of recorded samples after 1 policy step: {buffer._size}\n") -for i in range(buffer._size): - print(f"Recorded value {i}: {buffer.buf[i]}") -``` \ No newline at end of file +Observables can also be used to model sensor corruption and delay, and refer the reader to the [Sensor Randomization](../sim2real/sensor_randomization.md) section for additional information. diff --git a/docs/sim2real/dynamics_randomization.md b/docs/sim2real/dynamics_randomization.md new file mode 100644 index 0000000000..049197ca0b --- /dev/null +++ b/docs/sim2real/dynamics_randomization.md @@ -0,0 +1,71 @@ +# Dynamics Randomization + +In order to achieve reasonable runtime speeds, many physics simulation platforms often must simplify the underlying physics model. Mujoco is no different, and as a result, many parameters such as friction, damping, and contact constraints do not fully capture real-world dynamics. + +To better compensate for this, **robosuite** provides the `DynamicsModder` class, which can control individual dynamics parameters for each model within an environment. Theses parameters are sorted by element group, and briefly described below (for more information, please see [Mujoco XML Reference](http://www.mujoco.org/book/XMLreference.html)): + +#### Opt (Global) Parameters +- `density`: Density of the medium (i.e.: air) +- `viscosity`: Viscosity of the medium (i.e.: air) + +#### Body Parameters +- `position`: (x, y, z) Position of the body relative to its parent body +- `quaternion`: (qw, qx, qy, qz) Quaternion of the body relative to its parent body +- `inertia`: (ixx, iyy, izz) diagonal components of the inertia matrix associated with this body +- `mass`: mass of the body + +#### Geom Parameters +- `friction`: (sliding, torsional, rolling) friction values for this geom +- `solref`: (timeconst, dampratio) contact solver values for this geom +- `solimp`: (dmin, dmax, width, midpoint, power) contact solver impedance values for this geom + +#### Joint parameters +- `stiffness`: Stiffness for this joint +- `frictionloss`: Friction loss associated with this joint +- `damping`: Damping value for this joint + +This `DynamicsModder` follows the same basic API as the other `Modder` classes, and allows per-parameter and per-group randomization enabling. Apart from randomization, this modder can also be instantiated to selectively modify values at runtime. A brief example is given below: + +```python +import robosuite as suite +from robosuite.utils.mjmod import DynamicsModder +import numpy as np + +# Create environment and modder +env = suite.make("Lift", robots="Panda") +modder = DynamicsModder(sim=env.sim, random_state=np.random.RandomState(5)) + +# Define function for easy printing +cube_body_id = env.sim.model.body_name2id(env.cube.root_body) +cube_geom_ids = [env.sim.model.geom_name2id(geom) for geom in env.cube.contact_geoms] + +def print_params(): + print(f"cube mass: {env.sim.model.body_mass[cube_body_id]}") + print(f"cube frictions: {env.sim.model.geom_friction[cube_geom_ids]}") + print() + +# Print out initial parameter values +print("INITIAL VALUES") +print_params() + +# Modify the cube's properties +modder.mod(env.cube.root_body, "mass", 5.0) # make the cube really heavy +for geom_name in env.cube.contact_geoms: + modder.mod(geom_name, "friction", [2.0, 0.2, 0.04]) # greatly increase the friction +modder.update() # make sure the changes propagate in sim + +# Print out modified parameter values +print("MODIFIED VALUES") +print_params() + +# We can also restore defaults (original values) at any time +modder.restore_defaults() + +# Print out restored initial parameter values +print("RESTORED VALUES") +print_params() +``` + +Running [demo_domain_randomization.py](../demos.html#domain-randomization) is another method for demo'ing (albeit an extreme example of) this functionality. + +Note that the modder already has some sanity checks in place to prevent presumably undesired / non-sensical behavior, such as adding damping / frictionloss to a free joint or setting a non-zero stiffness value to a joint that is normally non-stiff to begin with. diff --git a/docs/sim2real/sensor_randomization.md b/docs/sim2real/sensor_randomization.md new file mode 100644 index 0000000000..1308e34344 --- /dev/null +++ b/docs/sim2real/sensor_randomization.md @@ -0,0 +1,68 @@ +# Sensor Randomization + +By default, Mujoco sensors are deterministic and delay-free, which is often an unrealistic assumption to make in the real world. To better close this domain gap, **robosuite** provides a realistic, customizable interface via the [Observable](../source/robosuite.utils.html#module-robosuite.utils.observables) class API. Observables model realistic sensor sampling, in which ground truth data is sampled (`sensor`), passed through a corrupting function (`corrupter`), and then finally passed through a filtering function (`filter`). Moreover, each observable has its own `sampling_rate` and `delayer` function which simulates sensor delay. While default values are used to instantiate each observable during environment creation, each of these components can be modified by the user at runtime using `env.modify_observable(...)` . Moreover, each observable is assigned a modality, and are grouped together in the returned observation dictionary during the `env.step()` call. For example, if an environment consists of camera observations and a single robot's proprioceptive observations, the observation dict structure might look as follows: + +```python +{ + "frontview_image": np.array(...), # this has modality "image" + "frontview_depth": np.array(...), # this has modality "image" + "robot0_joint_pos": np.array(...), # this has modality "robot0_proprio" + "robot0_gripper_pos": np.array(...), # this has modality "robot0_proprio" + "image-state": np.array(...), # this is a concatenation of all image observations + "robot0_proprio-state": np.array(...), # this is a concatenation of all robot0_proprio observations +} +``` + +Note that for memory efficiency the `image-state` is not returned by default (this can be toggled in `robosuite/utils/macros.py`). + +We showcase how the `Observable` functionality can be used to model sensor corruption and delay via [demo_sensor_corruption.py](../demos.html#sensor-realism). We also highlight that each of the `sensor`, `corrupter`, and `filter` functions can be arbitrarily specified to suit the end-user's usage. For example, a common use case for these observables is to keep track of sampled values from a sensor operating at a higher frequency than the environment step (control) frequency. In this case, the `filter` function can be leveraged to keep track of the real-time sensor values as they're being sampled. We provide a minimal script showcasing this ability below: + +```python +import robosuite as suite +import numpy as np +from robosuite.utils.buffers import RingBuffer + +# Create env instance +control_freq = 10 +env = suite.make("Lift", robots="Panda", has_offscreen_renderer=False, use_camera_obs=False, control_freq=control_freq) + +# Define a ringbuffer to store joint position values +buffer = RingBuffer(dim=env.robots[0].robot_model.dof, length=10) + +# Create a function that we'll use as the "filter" for the joint position Observable +# This is a pass-through operation, but we record the value every time it gets called +# As per the Observables API, this should take in an arbitrary numeric and return the same type / shape +def filter_fcn(corrupted_value): + # Record the inputted value + buffer.push(corrupted_value) + # Return this value (no-op performed) + return corrupted_value + +# Now, let's enable the joint position Observable with this filter function +env.modify_observable( + observable_name="robot0_joint_pos", + attribute="filter", + modifier=filter_fcn, +) + +# Let's also increase the sampling rate to showcase the Observable's ability to update multiple times per env step +obs_sampling_freq = control_freq * 4 +env.modify_observable( + observable_name="robot0_joint_pos", + attribute="sampling_rate", + modifier=obs_sampling_freq, +) + +# Take a single environment step with positive joint velocity actions +arm_action = np.ones(env.robots[0].robot_model.dof) * 1.0 +gripper_action = [1] +action = np.concatenate([arm_action, gripper_action]) +env.step(action) + +# Now we can analyze what values were recorded +np.set_printoptions(precision=2) +print(f"\nPolicy Frequency: {control_freq}, Observable Sampling Frequency: {obs_sampling_freq}") +print(f"Number of recorded samples after 1 policy step: {buffer._size}\n") +for i in range(buffer._size): + print(f"Recorded value {i}: {buffer.buf[i]}") +``` \ No newline at end of file diff --git a/docs/sim2real/visual_randomization.md b/docs/sim2real/visual_randomization.md new file mode 100644 index 0000000000..5153ee2a84 --- /dev/null +++ b/docs/sim2real/visual_randomization.md @@ -0,0 +1,9 @@ +# Visual Randomization + +It is well shown that randomizing the visuals in simulation can play an important role in sim2real applications. **robosuite** provides various `Modder` classes to control different aspects of the visual environment. This includes: + +- `CameraModder`: Modder for controlling camera parameters, including FOV and pose +- `TextureModder`: Modder for controlling visual objects' appearances, including texture and material properties +- `LightingModder`: Modder for controlling lighting parameters, including light source properties and pose + +Each of these Modders can be used by the user to directly override default simulation settings, or to randomize their respective properties mid-sim. We provide [demo_domain_randomization.py](../demos.html#domain-randomization) to showcase all of these modders being applied to randomize an environment during every sim step. From aa496914147093f3c45d72324d8cbd9a9b0b9d89 Mon Sep 17 00:00:00 2001 From: Yuke Zhu Date: Sun, 14 Feb 2021 22:56:12 -0600 Subject: [PATCH 12/16] move sim2real to subsection under algorithms --- docs/algorithms/sim2real.md | 156 ++++++++++++++++++++++++ docs/index.rst | 9 +- docs/sim2real/dynamics_randomization.md | 71 ----------- docs/sim2real/sensor_randomization.md | 68 ----------- docs/sim2real/visual_randomization.md | 9 -- 5 files changed, 157 insertions(+), 156 deletions(-) create mode 100644 docs/algorithms/sim2real.md delete mode 100644 docs/sim2real/dynamics_randomization.md delete mode 100644 docs/sim2real/sensor_randomization.md delete mode 100644 docs/sim2real/visual_randomization.md diff --git a/docs/algorithms/sim2real.md b/docs/algorithms/sim2real.md new file mode 100644 index 0000000000..e2a06e180c --- /dev/null +++ b/docs/algorithms/sim2real.md @@ -0,0 +1,156 @@ +# Sim-to-Real Transfer +This page covers the randomization techniques to narrow the reality gap of our robotics simulation. These techniques, which concerns about [visual observations](#visuals), [system dynamics](#dynamics), and [sensors](#sensors), are employed to improve the efficacy of transferring our simulation-trained models to the real world. + + +## Visuals + +It is well shown that randomizing the visuals in simulation can play an important role in sim2real applications. **robosuite** provides various `Modder` classes to control different aspects of the visual environment. This includes: + +- `CameraModder`: Modder for controlling camera parameters, including FOV and pose +- `TextureModder`: Modder for controlling visual objects' appearances, including texture and material properties +- `LightingModder`: Modder for controlling lighting parameters, including light source properties and pose + +Each of these Modders can be used by the user to directly override default simulation settings, or to randomize their respective properties mid-sim. We provide [demo_domain_randomization.py](../demos.html#domain-randomization) to showcase all of these modders being applied to randomize an environment during every sim step. + + +## Dynamics + +In order to achieve reasonable runtime speeds, many physics simulation platforms often must simplify the underlying physics model. Mujoco is no different, and as a result, many parameters such as friction, damping, and contact constraints do not fully capture real-world dynamics. + +To better compensate for this, **robosuite** provides the `DynamicsModder` class, which can control individual dynamics parameters for each model within an environment. Theses parameters are sorted by element group, and briefly described below (for more information, please see [Mujoco XML Reference](http://www.mujoco.org/book/XMLreference.html)): + +#### Opt (Global) Parameters +- `density`: Density of the medium (i.e.: air) +- `viscosity`: Viscosity of the medium (i.e.: air) + +#### Body Parameters +- `position`: (x, y, z) Position of the body relative to its parent body +- `quaternion`: (qw, qx, qy, qz) Quaternion of the body relative to its parent body +- `inertia`: (ixx, iyy, izz) diagonal components of the inertia matrix associated with this body +- `mass`: mass of the body + +#### Geom Parameters +- `friction`: (sliding, torsional, rolling) friction values for this geom +- `solref`: (timeconst, dampratio) contact solver values for this geom +- `solimp`: (dmin, dmax, width, midpoint, power) contact solver impedance values for this geom + +#### Joint parameters +- `stiffness`: Stiffness for this joint +- `frictionloss`: Friction loss associated with this joint +- `damping`: Damping value for this joint + +This `DynamicsModder` follows the same basic API as the other `Modder` classes, and allows per-parameter and per-group randomization enabling. Apart from randomization, this modder can also be instantiated to selectively modify values at runtime. A brief example is given below: + +```python +import robosuite as suite +from robosuite.utils.mjmod import DynamicsModder +import numpy as np + +# Create environment and modder +env = suite.make("Lift", robots="Panda") +modder = DynamicsModder(sim=env.sim, random_state=np.random.RandomState(5)) + +# Define function for easy printing +cube_body_id = env.sim.model.body_name2id(env.cube.root_body) +cube_geom_ids = [env.sim.model.geom_name2id(geom) for geom in env.cube.contact_geoms] + +def print_params(): + print(f"cube mass: {env.sim.model.body_mass[cube_body_id]}") + print(f"cube frictions: {env.sim.model.geom_friction[cube_geom_ids]}") + print() + +# Print out initial parameter values +print("INITIAL VALUES") +print_params() + +# Modify the cube's properties +modder.mod(env.cube.root_body, "mass", 5.0) # make the cube really heavy +for geom_name in env.cube.contact_geoms: + modder.mod(geom_name, "friction", [2.0, 0.2, 0.04]) # greatly increase the friction +modder.update() # make sure the changes propagate in sim + +# Print out modified parameter values +print("MODIFIED VALUES") +print_params() + +# We can also restore defaults (original values) at any time +modder.restore_defaults() + +# Print out restored initial parameter values +print("RESTORED VALUES") +print_params() +``` + +Running [demo_domain_randomization.py](../demos.html#domain-randomization) is another method for demo'ing (albeit an extreme example of) this functionality. + +Note that the modder already has some sanity checks in place to prevent presumably undesired / non-sensical behavior, such as adding damping / frictionloss to a free joint or setting a non-zero stiffness value to a joint that is normally non-stiff to begin with. + + +## Sensors + +By default, Mujoco sensors are deterministic and delay-free, which is often an unrealistic assumption to make in the real world. To better close this domain gap, **robosuite** provides a realistic, customizable interface via the [Observable](../source/robosuite.utils.html#module-robosuite.utils.observables) class API. Observables model realistic sensor sampling, in which ground truth data is sampled (`sensor`), passed through a corrupting function (`corrupter`), and then finally passed through a filtering function (`filter`). Moreover, each observable has its own `sampling_rate` and `delayer` function which simulates sensor delay. While default values are used to instantiate each observable during environment creation, each of these components can be modified by the user at runtime using `env.modify_observable(...)` . Moreover, each observable is assigned a modality, and are grouped together in the returned observation dictionary during the `env.step()` call. For example, if an environment consists of camera observations and a single robot's proprioceptive observations, the observation dict structure might look as follows: + +```python +{ + "frontview_image": np.array(...), # this has modality "image" + "frontview_depth": np.array(...), # this has modality "image" + "robot0_joint_pos": np.array(...), # this has modality "robot0_proprio" + "robot0_gripper_pos": np.array(...), # this has modality "robot0_proprio" + "image-state": np.array(...), # this is a concatenation of all image observations + "robot0_proprio-state": np.array(...), # this is a concatenation of all robot0_proprio observations +} +``` + +Note that for memory efficiency the `image-state` is not returned by default (this can be toggled in `robosuite/utils/macros.py`). + +We showcase how the `Observable` functionality can be used to model sensor corruption and delay via [demo_sensor_corruption.py](../demos.html#sensor-realism). We also highlight that each of the `sensor`, `corrupter`, and `filter` functions can be arbitrarily specified to suit the end-user's usage. For example, a common use case for these observables is to keep track of sampled values from a sensor operating at a higher frequency than the environment step (control) frequency. In this case, the `filter` function can be leveraged to keep track of the real-time sensor values as they're being sampled. We provide a minimal script showcasing this ability below: + +```python +import robosuite as suite +import numpy as np +from robosuite.utils.buffers import RingBuffer + +# Create env instance +control_freq = 10 +env = suite.make("Lift", robots="Panda", has_offscreen_renderer=False, use_camera_obs=False, control_freq=control_freq) + +# Define a ringbuffer to store joint position values +buffer = RingBuffer(dim=env.robots[0].robot_model.dof, length=10) + +# Create a function that we'll use as the "filter" for the joint position Observable +# This is a pass-through operation, but we record the value every time it gets called +# As per the Observables API, this should take in an arbitrary numeric and return the same type / shape +def filter_fcn(corrupted_value): + # Record the inputted value + buffer.push(corrupted_value) + # Return this value (no-op performed) + return corrupted_value + +# Now, let's enable the joint position Observable with this filter function +env.modify_observable( + observable_name="robot0_joint_pos", + attribute="filter", + modifier=filter_fcn, +) + +# Let's also increase the sampling rate to showcase the Observable's ability to update multiple times per env step +obs_sampling_freq = control_freq * 4 +env.modify_observable( + observable_name="robot0_joint_pos", + attribute="sampling_rate", + modifier=obs_sampling_freq, +) + +# Take a single environment step with positive joint velocity actions +arm_action = np.ones(env.robots[0].robot_model.dof) * 1.0 +gripper_action = [1] +action = np.concatenate([arm_action, gripper_action]) +env.step(action) + +# Now we can analyze what values were recorded +np.set_printoptions(precision=2) +print(f"\nPolicy Frequency: {control_freq}, Observable Sampling Frequency: {obs_sampling_freq}") +print(f"Number of recorded samples after 1 policy step: {buffer._size}\n") +for i in range(buffer._size): + print(f"Recorded value {i}: {buffer.buf[i]}") +``` \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index 050051a115..c36c4093fd 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -59,16 +59,9 @@ Welcome to robosuite's documentation! algorithms/benchmarking algorithms/demonstrations + algorithms/sim2real algorithms/roboturk -.. toctree:: - :maxdepth: 1 - :caption: Sim2Real - - sim2real/sensor_randomization - sim2real/visual_randomization - sim2real/dynamics_randomization - .. toctree:: :maxdepth: 1 :caption: Miscellaneous diff --git a/docs/sim2real/dynamics_randomization.md b/docs/sim2real/dynamics_randomization.md deleted file mode 100644 index 049197ca0b..0000000000 --- a/docs/sim2real/dynamics_randomization.md +++ /dev/null @@ -1,71 +0,0 @@ -# Dynamics Randomization - -In order to achieve reasonable runtime speeds, many physics simulation platforms often must simplify the underlying physics model. Mujoco is no different, and as a result, many parameters such as friction, damping, and contact constraints do not fully capture real-world dynamics. - -To better compensate for this, **robosuite** provides the `DynamicsModder` class, which can control individual dynamics parameters for each model within an environment. Theses parameters are sorted by element group, and briefly described below (for more information, please see [Mujoco XML Reference](http://www.mujoco.org/book/XMLreference.html)): - -#### Opt (Global) Parameters -- `density`: Density of the medium (i.e.: air) -- `viscosity`: Viscosity of the medium (i.e.: air) - -#### Body Parameters -- `position`: (x, y, z) Position of the body relative to its parent body -- `quaternion`: (qw, qx, qy, qz) Quaternion of the body relative to its parent body -- `inertia`: (ixx, iyy, izz) diagonal components of the inertia matrix associated with this body -- `mass`: mass of the body - -#### Geom Parameters -- `friction`: (sliding, torsional, rolling) friction values for this geom -- `solref`: (timeconst, dampratio) contact solver values for this geom -- `solimp`: (dmin, dmax, width, midpoint, power) contact solver impedance values for this geom - -#### Joint parameters -- `stiffness`: Stiffness for this joint -- `frictionloss`: Friction loss associated with this joint -- `damping`: Damping value for this joint - -This `DynamicsModder` follows the same basic API as the other `Modder` classes, and allows per-parameter and per-group randomization enabling. Apart from randomization, this modder can also be instantiated to selectively modify values at runtime. A brief example is given below: - -```python -import robosuite as suite -from robosuite.utils.mjmod import DynamicsModder -import numpy as np - -# Create environment and modder -env = suite.make("Lift", robots="Panda") -modder = DynamicsModder(sim=env.sim, random_state=np.random.RandomState(5)) - -# Define function for easy printing -cube_body_id = env.sim.model.body_name2id(env.cube.root_body) -cube_geom_ids = [env.sim.model.geom_name2id(geom) for geom in env.cube.contact_geoms] - -def print_params(): - print(f"cube mass: {env.sim.model.body_mass[cube_body_id]}") - print(f"cube frictions: {env.sim.model.geom_friction[cube_geom_ids]}") - print() - -# Print out initial parameter values -print("INITIAL VALUES") -print_params() - -# Modify the cube's properties -modder.mod(env.cube.root_body, "mass", 5.0) # make the cube really heavy -for geom_name in env.cube.contact_geoms: - modder.mod(geom_name, "friction", [2.0, 0.2, 0.04]) # greatly increase the friction -modder.update() # make sure the changes propagate in sim - -# Print out modified parameter values -print("MODIFIED VALUES") -print_params() - -# We can also restore defaults (original values) at any time -modder.restore_defaults() - -# Print out restored initial parameter values -print("RESTORED VALUES") -print_params() -``` - -Running [demo_domain_randomization.py](../demos.html#domain-randomization) is another method for demo'ing (albeit an extreme example of) this functionality. - -Note that the modder already has some sanity checks in place to prevent presumably undesired / non-sensical behavior, such as adding damping / frictionloss to a free joint or setting a non-zero stiffness value to a joint that is normally non-stiff to begin with. diff --git a/docs/sim2real/sensor_randomization.md b/docs/sim2real/sensor_randomization.md deleted file mode 100644 index 1308e34344..0000000000 --- a/docs/sim2real/sensor_randomization.md +++ /dev/null @@ -1,68 +0,0 @@ -# Sensor Randomization - -By default, Mujoco sensors are deterministic and delay-free, which is often an unrealistic assumption to make in the real world. To better close this domain gap, **robosuite** provides a realistic, customizable interface via the [Observable](../source/robosuite.utils.html#module-robosuite.utils.observables) class API. Observables model realistic sensor sampling, in which ground truth data is sampled (`sensor`), passed through a corrupting function (`corrupter`), and then finally passed through a filtering function (`filter`). Moreover, each observable has its own `sampling_rate` and `delayer` function which simulates sensor delay. While default values are used to instantiate each observable during environment creation, each of these components can be modified by the user at runtime using `env.modify_observable(...)` . Moreover, each observable is assigned a modality, and are grouped together in the returned observation dictionary during the `env.step()` call. For example, if an environment consists of camera observations and a single robot's proprioceptive observations, the observation dict structure might look as follows: - -```python -{ - "frontview_image": np.array(...), # this has modality "image" - "frontview_depth": np.array(...), # this has modality "image" - "robot0_joint_pos": np.array(...), # this has modality "robot0_proprio" - "robot0_gripper_pos": np.array(...), # this has modality "robot0_proprio" - "image-state": np.array(...), # this is a concatenation of all image observations - "robot0_proprio-state": np.array(...), # this is a concatenation of all robot0_proprio observations -} -``` - -Note that for memory efficiency the `image-state` is not returned by default (this can be toggled in `robosuite/utils/macros.py`). - -We showcase how the `Observable` functionality can be used to model sensor corruption and delay via [demo_sensor_corruption.py](../demos.html#sensor-realism). We also highlight that each of the `sensor`, `corrupter`, and `filter` functions can be arbitrarily specified to suit the end-user's usage. For example, a common use case for these observables is to keep track of sampled values from a sensor operating at a higher frequency than the environment step (control) frequency. In this case, the `filter` function can be leveraged to keep track of the real-time sensor values as they're being sampled. We provide a minimal script showcasing this ability below: - -```python -import robosuite as suite -import numpy as np -from robosuite.utils.buffers import RingBuffer - -# Create env instance -control_freq = 10 -env = suite.make("Lift", robots="Panda", has_offscreen_renderer=False, use_camera_obs=False, control_freq=control_freq) - -# Define a ringbuffer to store joint position values -buffer = RingBuffer(dim=env.robots[0].robot_model.dof, length=10) - -# Create a function that we'll use as the "filter" for the joint position Observable -# This is a pass-through operation, but we record the value every time it gets called -# As per the Observables API, this should take in an arbitrary numeric and return the same type / shape -def filter_fcn(corrupted_value): - # Record the inputted value - buffer.push(corrupted_value) - # Return this value (no-op performed) - return corrupted_value - -# Now, let's enable the joint position Observable with this filter function -env.modify_observable( - observable_name="robot0_joint_pos", - attribute="filter", - modifier=filter_fcn, -) - -# Let's also increase the sampling rate to showcase the Observable's ability to update multiple times per env step -obs_sampling_freq = control_freq * 4 -env.modify_observable( - observable_name="robot0_joint_pos", - attribute="sampling_rate", - modifier=obs_sampling_freq, -) - -# Take a single environment step with positive joint velocity actions -arm_action = np.ones(env.robots[0].robot_model.dof) * 1.0 -gripper_action = [1] -action = np.concatenate([arm_action, gripper_action]) -env.step(action) - -# Now we can analyze what values were recorded -np.set_printoptions(precision=2) -print(f"\nPolicy Frequency: {control_freq}, Observable Sampling Frequency: {obs_sampling_freq}") -print(f"Number of recorded samples after 1 policy step: {buffer._size}\n") -for i in range(buffer._size): - print(f"Recorded value {i}: {buffer.buf[i]}") -``` \ No newline at end of file diff --git a/docs/sim2real/visual_randomization.md b/docs/sim2real/visual_randomization.md deleted file mode 100644 index 5153ee2a84..0000000000 --- a/docs/sim2real/visual_randomization.md +++ /dev/null @@ -1,9 +0,0 @@ -# Visual Randomization - -It is well shown that randomizing the visuals in simulation can play an important role in sim2real applications. **robosuite** provides various `Modder` classes to control different aspects of the visual environment. This includes: - -- `CameraModder`: Modder for controlling camera parameters, including FOV and pose -- `TextureModder`: Modder for controlling visual objects' appearances, including texture and material properties -- `LightingModder`: Modder for controlling lighting parameters, including light source properties and pose - -Each of these Modders can be used by the user to directly override default simulation settings, or to randomize their respective properties mid-sim. We provide [demo_domain_randomization.py](../demos.html#domain-randomization) to showcase all of these modders being applied to randomize an environment during every sim step. From a67c686e9e1db10b18f6c964a90eff82d61d91c2 Mon Sep 17 00:00:00 2001 From: josiah_wong <84cremebrule@gmail.com> Date: Wed, 17 Feb 2021 20:31:34 -0800 Subject: [PATCH 13/16] add warning about action playback, small dataset compatible with v1.2, small wording updates --- docs/algorithms/demonstrations.md | 7 ++++++- docs/algorithms/roboturk.md | 4 ++-- docs/algorithms/sim2real.md | 1 + 3 files changed, 9 insertions(+), 3 deletions(-) diff --git a/docs/algorithms/demonstrations.md b/docs/algorithms/demonstrations.md index 4beb0234e6..cd8f9fea40 100644 --- a/docs/algorithms/demonstrations.md +++ b/docs/algorithms/demonstrations.md @@ -81,4 +81,9 @@ The reason for storing mujoco states instead of raw observations is to make it e ## Using Demonstrations for Learning -[Several](https://arxiv.org/abs/1802.09564) [prior](https://arxiv.org/abs/1807.06919) [works](https://arxiv.org/abs/1804.02717) have demonstrated the effectiveness of altering the start state distribution of training episodes for learning RL policies. We provide a generic utility for setting various types of learning curriculums which dictate how to sample from demonstration episodes when doing an environment reset. For more information see the `DemoSamplerWrapper` class. \ No newline at end of file +[Several](https://arxiv.org/abs/1802.09564) [prior](https://arxiv.org/abs/1807.06919) [works](https://arxiv.org/abs/1804.02717) have demonstrated the effectiveness of altering the start state distribution of training episodes for learning RL policies. We provide a generic utility for setting various types of learning curriculums which dictate how to sample from demonstration episodes when doing an environment reset. For more information see the `DemoSamplerWrapper` class. + +## Warnings +We have verified that deterministic action playback works specifically when playing back demonstrations on the *same machine* that the demonstrations were originally collected upon. However, this means that deterministic action playback is NOT guaranteed (in fact, very unlikely) to work across platforms or even across different machines using the same OS. + +While action playback trajectories are quite similar even if not completely identical to the original collected state trajectories, they do tend to drift over time, and should not be relied upon to accurately replicate demonstrations. Instead, we recommend directly setting states to reproduce the collected trajectories, as shown in [playback_demonstrations_from_hdf5](https://github.com/ARISE-Initiative/robosuite/blob/master/robosuite/scripts/playback_demonstrations_from_hdf5.py). \ No newline at end of file diff --git a/docs/algorithms/roboturk.md b/docs/algorithms/roboturk.md index 8e23ebf840..c60163a0b5 100644 --- a/docs/algorithms/roboturk.md +++ b/docs/algorithms/roboturk.md @@ -2,9 +2,9 @@ [RoboTurk](https://roboturk.stanford.edu/) is a crowdsourcing platform developed in order to enabled collecting large-scale manipulation datasets. Below, we describe RoboTurk datasets that are compatible with robosuite. -## Updated Datasets compatible with v1.0 +## Updated Datasets compatible with v1.0+ -We are currently in the process of reformatting the demonstrations. These datasets will be made available soon. +We are currently in the process of organizing a standardized dataset for our benchmarking tasks, which will be made available soon and compatible with v1.2.0+. In the meantime, we have provided a [small-scale dataset](https://drive.google.com/drive/folders/1LLkuFnRdqQ6xn1cYzkbJUs_DreaAvN7i?usp=sharing) of expert demonstrations on two of our tasks. ## Original Datasets compatible with v0.3 diff --git a/docs/algorithms/sim2real.md b/docs/algorithms/sim2real.md index e2a06e180c..337d7b856e 100644 --- a/docs/algorithms/sim2real.md +++ b/docs/algorithms/sim2real.md @@ -38,6 +38,7 @@ To better compensate for this, **robosuite** provides the `DynamicsModder` class - `stiffness`: Stiffness for this joint - `frictionloss`: Friction loss associated with this joint - `damping`: Damping value for this joint +- `armature`: Gear inertia for this joint This `DynamicsModder` follows the same basic API as the other `Modder` classes, and allows per-parameter and per-group randomization enabling. Apart from randomization, this modder can also be instantiated to selectively modify values at runtime. A brief example is given below: From f14fbd9ec3d3a6992f5a683af4e0ebf794d6f6bf Mon Sep 17 00:00:00 2001 From: josiah_wong <84cremebrule@gmail.com> Date: Wed, 17 Feb 2021 20:32:00 -0800 Subject: [PATCH 14/16] update README with v1.2 announcement --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 89aae0fa9d..2f7fc20ec4 100644 --- a/README.md +++ b/README.md @@ -6,6 +6,8 @@ ------- ## Latest Updates +[02/17/2021] **v1.2.0**: Added observable sensor models :eyes: and dynamics randomization :game_die: + [12/17/2020] **v1.1.0**: Refactored infrastructure and standardized model classes for much easier environment prototyping :wrench: ------- From 8356a6afa66388912c6bcaaa9f5dff2693cd541e Mon Sep 17 00:00:00 2001 From: josiah_wong <84cremebrule@gmail.com> Date: Wed, 17 Feb 2021 20:59:38 -0800 Subject: [PATCH 15/16] fix broken links and docstring --- docs/modules/sensors.md | 2 +- robosuite/utils/observables.py | 45 +++++++++++++++++++++++++--------- 2 files changed, 34 insertions(+), 13 deletions(-) diff --git a/docs/modules/sensors.md b/docs/modules/sensors.md index c0bd88e0b8..0fd6b39a3c 100644 --- a/docs/modules/sensors.md +++ b/docs/modules/sensors.md @@ -27,4 +27,4 @@ Cameras bundle a name to a set of properties to render images of the environment Note that for memory efficiency the `image-state` is not returned by default (this can be toggled in `robosuite/utils/macros.py`). -Observables can also be used to model sensor corruption and delay, and refer the reader to the [Sensor Randomization](../sim2real/sensor_randomization.md) section for additional information. +Observables can also be used to model sensor corruption and delay, and refer the reader to the [Sensor Randomization](../algorithms/sim2real.html#sensors) section for additional information. diff --git a/robosuite/utils/observables.py b/robosuite/utils/observables.py index 3e9beaad6c..c55d51e826 100644 --- a/robosuite/utils/observables.py +++ b/robosuite/utils/observables.py @@ -14,18 +14,21 @@ def sensor(modality): An example use case is shown below: - @sensor(modality="proprio") - def joint_pos(obs_cache): - # Always handle case if obs_cache is empty - if not obs_cache: - return np.zeros(7) - # Otherwise, run necessary calculations and return output - ... - out = ... - return out + >>> @sensor(modality="proprio") + >>> def joint_pos(obs_cache): + # Always handle case if obs_cache is empty + if not obs_cache: + return np.zeros(7) + # Otherwise, run necessary calculations and return output + ... + out = ... + return out Args: modality (str): Modality for this sensor + + Returns: + function: decorator function """ # Define standard decorator (with no args) def decorator(func): @@ -44,6 +47,9 @@ def create_deterministic_corrupter(corruption, low=-np.inf, high=np.inf): corruption (float): Corruption to apply low (float): Minimum value for output for clipping high (float): Maximum value for output for clipping + + Returns: + function: corrupter """ def corrupter(inp): inp = np.array(inp) @@ -60,6 +66,9 @@ def create_uniform_noise_corrupter(min_noise, max_noise, low=-np.inf, high=np.in max_noise (float): Maximum noise to apply low (float): Minimum value for output for clipping high (float): Maxmimum value for output for clipping + + Returns: + function: corrupter """ def corrupter(inp): inp = np.array(inp) @@ -77,6 +86,9 @@ def create_gaussian_noise_corrupter(mean, std, low=-np.inf, high=np.inf): std (float): Standard deviation of the noise to apply low (float): Minimum value for output for clipping high (float): Maxmimum value for output for clipping + + Returns: + function: corrupter """ def corrupter(inp): inp = np.array(inp) @@ -91,6 +103,9 @@ def create_deterministic_delayer(delay): Args: delay (float): Delay value to return + + Returns: + function: delayer """ assert delay >= 0, "Inputted delay must be non-negative!" return lambda: delay @@ -103,6 +118,9 @@ def create_uniform_sampled_delayer(min_delay, max_delay): Args: min_delay (float): Minimum possible delay max_delay (float): Maxmimum possible delay + + Returns: + function: delayer """ assert min(min_delay, max_delay) >= 0, "Inputted delay must be non-negative!" return lambda: min_delay + (max_delay - min_delay) * np.random.random() @@ -115,6 +133,9 @@ def create_gaussian_sampled_delayer(mean, std): Args: mean (float): Average delay std (float): Standard deviation of the delay variation + + Returns: + function: delayer """ assert mean >= 0, "Inputted mean delay must be non-negative!" return lambda: max(0.0, int(np.round(mean + std * np.random.randn()))) @@ -132,7 +153,7 @@ class Observable: Args: name (str): Name for this observable - sensor (function with sensor decorator): Method to grab raw sensor data for this observable. Should take in a + sensor (function with `sensor` decorator): Method to grab raw sensor data for this observable. Should take in a single dict argument (observation cache if a pre-computed value is required) and return the raw sensor data for the current timestep. Must handle case if inputted argument is empty ({}), and should have `sensor` decorator when defined @@ -247,7 +268,7 @@ def is_enabled(self): def is_active(self): """ Determines whether observable is active or not. This observable is considered active if its current observation - value is being returned in self.obs. + value is being returned in self.obs. Returns: bool: True if this observable is active @@ -269,7 +290,7 @@ def set_enabled(self, enabled): def set_active(self, active): """ Sets whether this observable is active or not. If active, this observable's current - observed value is returned from self.obs, otherwise self.obs returns None. + observed value is returned from self.obs, otherwise self.obs returns None. Args: active (bool): True if this observable should be active From d83a4f181e31c09e848510989313562f6d830824 Mon Sep 17 00:00:00 2001 From: josiah_wong <84cremebrule@gmail.com> Date: Wed, 17 Feb 2021 21:32:00 -0800 Subject: [PATCH 16/16] fix broken links and update docstring --- docs/modules/environments.md | 2 +- docs/modules/objects.md | 12 ++++++------ docs/modules/sensors.md | 2 +- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/modules/environments.md b/docs/modules/environments.md index e67cfe5472..6b841f8239 100644 --- a/docs/modules/environments.md +++ b/docs/modules/environments.md @@ -71,7 +71,7 @@ env = robosuite.make( We provide a few additional details on a few keyword arguments below to highlight the modular structure of creating **robosuite** environments, and how easy it is to configure different environment features. -- `robots` : this argument can be used to easily instantiate tasks with different robot arms. For example, we could change the task to use two "Jaco" robots by passing `robots=["Jaco", "Jaco"]`. Once the environment is initialized, these robots (as captured by the [Robot](robots) class) can be accessed via the `robots` array attribute within the environment, i.e.: `env.robots[i]` for the `ith` robot arm in the environment. +- `robots` : this argument can be used to easily instantiate tasks with different robot arms. For example, we could change the task to use two "Jaco" robots by passing `robots=["Jaco", "Jaco"]`. Once the environment is initialized, these robots (as captured by the [Robot](../simulation/robot.html#robot) class) can be accessed via the `robots` array attribute within the environment, i.e.: `env.robots[i]` for the `ith` robot arm in the environment. - `gripper_types` : this argument can be used to easily swap out different grippers for each robot arm. For example, suppose we want to swap the default grippers for the arms in the example above. We could just pass `gripper_types=["PandaGripper", "RethinkGripper"]` to achieve this. Note that a single type can also be used to automatically broadcast the same gripper type across all arms. - `controller_configs` : this argument can be used to easily replace the action space for each robot arm. For example, if we would like to control the arm using joint velocities instead of OSC, we could use `load_controller_config(default_controller="JOINT_VELOCITY")` in the example above. Similar to `gripper_types` this value can either be per-arm specific or a single configuration to broadcast to all robot arms. - `env_configuration` : this argument is mainly used for two-arm tasks to easily configure how the robots are oriented with respect to one another. For example, in the `TwoArmLift` environment, we could pass `env_configuration="single-arm-parallel"` instead so that the robot arms are located next to each other, instead of opposite each other diff --git a/docs/modules/objects.md b/docs/modules/objects.md index d64edc81ae..99986df6d6 100644 --- a/docs/modules/objects.md +++ b/docs/modules/objects.md @@ -1,7 +1,7 @@ # Objects ## How to create a custom object -Objects, such as boxes and cans, are essential to building manipulation environments. We designed the [MujocoObject](../../../robosuite/models/objects/objects.py) interfaces to standardize and simplify the procedure for importing 3D models into the scene or procedurally generate new objects. MuJoCo defines models via the [MJCF](http://www.mujoco.org/book/modeling.html) XML format. These MJCF files can either be stored as XML files on disk and loaded into simulator, or be created on-the-fly by code prior to simulation. Based on these two mechanisms of how MJCF models are created, we offer two main ways of creating your own object: +Objects, such as boxes and cans, are essential to building manipulation environments. We designed the [MujocoObject](../source/robosuite.models.objects.html#robosuite.models.objects.objects.MujocoObject) interfaces to standardize and simplify the procedure for importing 3D models into the scene or procedurally generate new objects. MuJoCo defines models via the [MJCF](http://www.mujoco.org/book/modeling.html) XML format. These MJCF files can either be stored as XML files on disk and loaded into simulator, or be created on-the-fly by code prior to simulation. Based on these two mechanisms of how MJCF models are created, we offer two main ways of creating your own object: * Define an object in an MJCF XML file; * Use procedural generation APIs to dynamically create an MJCF model. @@ -57,7 +57,7 @@ obj_xml = obj.get_obj().set("pos", array_to_string(pos)) # Set the top-level ``` ## Creating a XMLObject -One can use MuJoCo MJCF XML to generate an object, either as a composition of primitive [geoms](http://mujoco.org/book/modeling.html#geom) or imported from STL files of triangulated [meshes](http://www.mujoco.org/book/modeling.html#mesh). An example is `robosuite.models.objects.xml_objects.BreadObject`. Its [python definition](../../../robosuite/models/objects/xml_objects.py#L41) is short. Note that all `MujocoXMLObject` classes require both a `fname` and `name` argument, the former which specifies the filepath to the raw XML file and the latter which specifies the in-sim name of the object instantiated. The optional `joints` argument can also specify a custom set of joints to apply to the given object (defaults to "default", which is a single free joint). Generally, the normal use case is to define a single class corresponding to a specific XML file, as shown below: +One can use MuJoCo MJCF XML to generate an object, either as a composition of primitive [geoms](http://mujoco.org/book/modeling.html#geom) or imported from STL files of triangulated [meshes](http://www.mujoco.org/book/modeling.html#mesh). An example is `robosuite.models.objects.xml_objects.BreadObject`. Its [python definition](https://github.com/ARISE-Initiative/robosuite/blob/master/robosuite/models/objects/xml_objects.py#L49) is short. Note that all `MujocoXMLObject` classes require both a `fname` and `name` argument, the former which specifies the filepath to the raw XML file and the latter which specifies the in-sim name of the object instantiated. The optional `joints` argument can also specify a custom set of joints to apply to the given object (defaults to "default", which is a single free joint). Additionally, the type of object created can be specified via the `obj_type` argument, and must be one of (`'collision'`, `'visual'`, or `'all'`). Lastly, setting `duplicate_collision_geoms` makes sure that all collision geoms automatically have an associated visual geom as well. Generally, the normal use case is to define a single class corresponding to a specific XML file, as shown below: ```python class BreadObject(MujocoXMLObject): def __init__(self, name): @@ -66,7 +66,7 @@ class BreadObject(MujocoXMLObject): obj_type="all", duplicate_collision_geoms=True) ``` -In the corresponding XML file, a few key definitions must be present. The top-level, un-named body must contain as immediate children tags (a) the actual object bodie(s) (the top-level **must** be named `object`) and (b) three site tags named `bottom_site`, `top_site`, and `horizontal_radius_site` and whose `pos` values must be specified. The example for the `BreadObject`, [bread.xml](../robosuite/models/assets/objects/bread.xml), is shown below: +In the corresponding XML file, a few key definitions must be present. The top-level, un-named body must contain as immediate children tags (a) the actual object bodie(s) (the top-level **must** be named `object`) and (b) three site tags named `bottom_site`, `top_site`, and `horizontal_radius_site` and whose `pos` values must be specified. The example for the `BreadObject`, [bread.xml](https://github.com/ARISE-Initiative/robosuite/blob/master/robosuite/models/assets/objects/bread.xml), is shown below: ```xml @@ -96,13 +96,13 @@ Concretely, Procedurally generated objects have been used in [several](https://arxiv.org/abs/1802.09564) [recent](https://arxiv.org/abs/1806.09266) [works](https://arxiv.org/abs/1709.07857) to train control policies with improved robustness and generalization. Here you can programmatically generate an MJCF XML of an object from scratch using `xml.etree.ElementTree`, and compose an object of multiple geom primitives. The base class for this type of object is `MujocoGeneratedObject`. **robosuite** natively supports all Mujoco primitive objects with procedurally-generated `PrimitiveObject` classes (`BoxObject`, `BallObject`, `CapsuleObject`, and `CylinderObject`). -Additionally, **robosuite** supports custom, complex objects that can be defined by collections of primitive geoms (the [`CompositeObject`](../../../robosuite/models/objects/generated_objects.py#L257) class) or even other objects (the [`CompositeBodyObject`](../../../robosuite/models/objects/generated_objects.py#L10) class). The APIs for each of these classes have been standardized for ease of usage, and interested readers should consult the docstrings for each of these classes, as well as provided examples of each class ([`HammerObject`](../../../robosuite/models/objects/composite/hammer.py), [`HingedBoxObject`](../../../robosuite/models/objects/composite_body/hinged_box.py)). +Additionally, **robosuite** supports custom, complex objects that can be defined by collections of primitive geoms (the [CompositeObject](../source/robosuite.models.objects.html#robosuite.models.objects.generated_objects.CompositeObject) class) or even other objects (the [CompositeBodyObject](../source/robosuite.models.objects.html#robosuite.models.objects.generated_objects.CompositeBodyObject) class). The APIs for each of these classes have been standardized for ease of usage, and interested readers should consult the docstrings for each of these classes, as well as provided examples of each class ([HammerObject](https://github.com/ARISE-Initiative/robosuite/blob/master/robosuite/models/objects/composite/hammer.py#L10), [HingedBoxObject](https://github.com/ARISE-Initiative/robosuite/blob/master/robosuite/models/objects/composite_body/hinged_box.py#L8)). It should also be noted that all of the above classes extending from the `MujocoGenereatedObject` class automatically supports custom texture definitions on a per-geom level, where specific texture images can be mapped to individual geoms. The above `HammerObject` showcases an example applying custom textures to different geoms of the resulting object. ## Placing Objects -Object locations are initialized on every environment reset using instances of the `ObjectPositionSampler` class. Object samplers use the `bottom_site` and `top_site` sites of each object in order to place objects on top of other objects, and the `horizontal_radius_site` site in order to ensure that objects do not collide with one another. The most basic sampler is the `UniformRandomSampler` class - this just uses rejection sampling to place objects randomly. As an example, consider the following code snippet from the `__init__` method of the `Lift` environment class. +Object locations are initialized on every environment reset using instances of the [ObjectPositionSampler](../source/robosuite.utils.html#robosuite.utils.placement_samplers.ObjectPositionSampler) class. Object samplers use the `bottom_site` and `top_site` sites of each object in order to place objects on top of other objects, and the `horizontal_radius_site` site in order to ensure that objects do not collide with one another. The most basic sampler is the [UniformRandomSampler](../source/robosuite.utils.html#robosuite.utils.placement_samplers.UniformRandomSampler) class - this just uses rejection sampling to place objects randomly. As an example, consider the following code snippet from the `__init__` method of the `Lift` environment class. ```python self.placement_initializer = UniformRandomSampler( @@ -121,7 +121,7 @@ self.placement_initializer = UniformRandomSampler( This will sample the `self.cube`'s object location uniformly at random in a box of size `0.03` (`x_range`, `y_range`) with random (`rotation`) z-rotation (`rotation_axis`), and with an offset of `0.01` (`z_offset`) above the table surface location (`reference_pos`). The sampler will also make sure that the entire object boundary falls within the sampling box size (`ensure_object_boundary_in_range`) and does not collide with any placed objects (`ensure_valid_placement`). -Another common sampler is the `SequentialCompositeSampler`, which is useful for composing multiple arbitrary placement samplers together. As an example, consider the following code snippet from the `__init__` method of the `NutAssembly` environment class. +Another common sampler is the [SequentialCompositeSampler](../source/robosuite.utils.html#robosuite.utils.placement_samplers.SequentialCompositeSampler), which is useful for composing multiple arbitrary placement samplers together. As an example, consider the following code snippet from the `__init__` method of the `NutAssembly` environment class. ```python # Establish named references to each nut object diff --git a/docs/modules/sensors.md b/docs/modules/sensors.md index 0fd6b39a3c..f97a419566 100644 --- a/docs/modules/sensors.md +++ b/docs/modules/sensors.md @@ -12,7 +12,7 @@ Cameras bundle a name to a set of properties to render images of the environment #### Observables -**robosuite** provides a realistic, customizable interface via the [Observable](../source/robosuite.utils.html#module-robosuite.utils.observables) class API. Observables model realistic sensor sampling, in which ground truth data is sampled (`sensor`), passed through a corrupting function (`corrupter`), and then finally passed through a filtering function (`filter`). Moreover, each observable has its own `sampling_rate` and `delayer` function which simulates sensor delay. While default values are used to instantiate each observable during environment creation, each of these components can be modified by the user at runtime using `env.modify_observable(...)` . Moreover, each observable is assigned a modality, and are grouped together in the returned observation dictionary during the `env.step()` call. For example, if an environment consists of camera observations and a single robot's proprioceptive observations, the observation dict structure might look as follows: +**robosuite** provides a realistic, customizable interface via the [Observable](../source/robosuite.utils.html#robosuite.utils.observables.Observable) class API. Observables model realistic sensor sampling, in which ground truth data is sampled (`sensor`), passed through a corrupting function (`corrupter`), and then finally passed through a filtering function (`filter`). Moreover, each observable has its own `sampling_rate` and `delayer` function which simulates sensor delay. While default values are used to instantiate each observable during environment creation, each of these components can be modified by the user at runtime using `env.modify_observable(...)` . Moreover, each observable is assigned a modality, and are grouped together in the returned observation dictionary during the `env.step()` call. For example, if an environment consists of camera observations and a single robot's proprioceptive observations, the observation dict structure might look as follows: ```python {