dfki-ric · AlexanderFabisch · Nov 28, 2023 · Nov 24, 2023 · Nov 24, 2023 · Nov 24, 2023
diff --git a/README.md b/README.md
@@ -4,10 +4,28 @@
 
 # Movement Primitives
 
+> Dynamical movement primitives (DMPs), probabilistic movement primitives
+> (ProMPs), and spatially coupled bimanual DMPs for imitation learning.
+
 Movement primitives are a common group of policy representations in robotics.
 There are many types and variations. This repository focuses mainly on
-imitation learning, generalization, and adaptation of movement primitives.
-It provides implementations in Python and Cython.
+imitation learning, generalization, and adaptation of movement primitives for
+Cartesian motions of robots. It provides implementations in Python and Cython
+and can be installed directly from
+[PyPI](https://pypi.org/project/movement-primitives/).
+
+## Content
+
+* [Features](#features)
+* [API Documentation](#api-documentation)
+* [Install Library](#install-library)
+* [Examples](#examples)
+* [Build API Documentation](#build-api-documentation)
+* [Test](#test)
+* [Contributing](#contributing)
+* [Non-public Extensions](#non-public-extensions)
+* [Related Publications](#related-publications)
+* [Funding](#funding)
 
 ## Features
 
@@ -19,14 +37,10 @@ It provides implementations in Python and Cython.
 * Propagation of DMP weight distribution to state space distribution
 * Probabilistic Movement Primitives (ProMPs)
 
-Example of dual Cartesian DMP with
-[RH5 Manus](https://robotik.dfki-bremen.de/en/research/robot-systems/rh5-manus/):
-
-<img src="https://raw.githubusercontent.com/dfki-ric/movement_primitives/main/doc/source/_static/dual_cart_dmp_rh5_with_panel.gif" width="256px" />
-
-Example of joint space DMP with UR5:
+<img src="https://raw.githubusercontent.com/dfki-ric/movement_primitives/main/doc/source/_static/dual_cart_dmp_rh5_with_panel.gif" height="200px" /><img src="https://raw.githubusercontent.com/dfki-ric/movement_primitives/main/doc/source/_static/dmp_ur5_minimum_jerk.gif" height="200px" />
 
-<img src="https://raw.githubusercontent.com/dfki-ric/movement_primitives/main/doc/source/_static/dmp_ur5_minimum_jerk.gif" width="256px" />
+Left: Example of dual Cartesian DMP with [RH5 Manus](https://robotik.dfki-bremen.de/en/research/robot-systems/rh5-manus/).
+Right: Example of joint space DMP with UR5.
 
 ## API Documentation
 
@@ -71,83 +85,12 @@ or install the library with
 python setup.py install
 ```
 
-## Non-public Extensions
-
-Scripts from the subfolder `examples/external_dependencies/` require access to
-git repositories (URDF files or optional dependencies) and datasets that are
-not publicly available. They are available on request (email
-alexander.fabisch@dfki.de).
-
-Note that the library does not have any non-public dependencies! They are only
-required to run all examples.
-
-### MoCap Library
-
-```bash
-# untested: pip install git+https://git.hb.dfki.de/dfki-interaction/mocap.git
-git clone git@git.hb.dfki.de:dfki-interaction/mocap.git
-cd mocap
-python -m pip install -e .
-cd ..
-```
-
-### Get URDFs
-
-```bash
-# RH5
-git clone git@git.hb.dfki.de:models-robots/rh5_models/pybullet-only-arms-urdf.git --recursive
-# RH5v2
-git clone git@git.hb.dfki.de:models-robots/rh5v2_models/pybullet-urdf.git --recursive
-# Kuka
-git clone git@git.hb.dfki.de:models-robots/kuka_lbr.git
-# Solar panel
-git clone git@git.hb.dfki.de:models-objects/solar_panels.git
-# RH5 Gripper
-git clone git@git.hb.dfki.de:motto/abstract-urdf-gripper.git --recursive
-```
-
-### Data
-
-I assume that your data is located in the folder `data/` in most scripts.
-You should put a symlink there to point to your actual data folder.
-
-## Build API Documentation
-
-You can build an API documentation with [pdoc3](https://pdoc3.github.io/pdoc/).
-You can install pdoc3 with
-
-```bash
-pip install pdoc3
-```
-
-... and build the documentation from the main folder with
-
-```bash
-pdoc movement_primitives --html
-```
-
-It will be located at `html/movement_primitives/index.html`.
-
-## Test
-
-To run the tests some python libraries are required:
-
-```bash
-python -m pip install -e .[test]
-```
-
-The tests are located in the folder `test/` and can be executed with:
-`python -m nose test`
-
-This command searches for all files with `test` and executes the functions with `test_*`.
-
-## Contributing
-
-To add new features, documentation, or fix bugs you can open a pull request.
-Directly pushing to the main branch is not allowed.
-
 ## Examples
 
+You will find a lot of examples in the subfolder
+[`examples/`](https://github.com/dfki-ric/movement_primitives/tree/main/examples).
+Here are just some highlights to showcase the library.
+
 ### Conditional ProMPs
 
 <img src="https://raw.githubusercontent.com/dfki-ric/movement_primitives/main/doc/source/_static/conditional_promps.png" width="800px" />
@@ -157,7 +100,7 @@ trajectories that can be conditioned on viapoints. In this example, we
 plot the resulting posterior distribution after conditioning on varying
 start positions.
 
-[Script](examples/plot_conditional_promp.py)
+[Script](https://github.com/dfki-ric/movement_primitives/blob/main/examples/plot_conditional_promp.py)
 
 ### Potential Field of 2D DMP
 
@@ -167,7 +110,7 @@ A Dynamical Movement Primitive defines a potential field that superimposes
 several components: transformation system (goal-directed movement), forcing
 term (learned shape), and coupling terms (e.g., obstacle avoidance).
 
-[Script](examples/plot_dmp_potential_field.py)
+[Script](https://github.com/dfki-ric/movement_primitives/blob/main/examples/plot_dmp_potential_field.py)
 
 ### DMP with Final Velocity
 
@@ -177,7 +120,7 @@ Not all DMPs allow a final velocity > 0. In this case we analyze the effect
 of changing final velocities in an appropriate variation of the DMP
 formulation that allows to set the final velocity.
 
-[Script](examples/plot_dmp_with_final_velocity.py)
+[Script](https://github.com/dfki-ric/movement_primitives/blob/main/examples/plot_dmp_with_final_velocity.py)
 
 ### ProMPs
 
@@ -188,7 +131,7 @@ The LASA Handwriting dataset learned with ProMPs. The dataset consists of
 demonstrations and the second and fourth column show the imitated ProMPs
 with 1-sigma interval.
 
-[Script](examples/plot_promp_lasa.py)
+[Script](https://github.com/dfki-ric/movement_primitives/blob/main/examples/plot_promp_lasa.py)
 
 ### Cartesian DMPs
 
@@ -197,7 +140,7 @@ with 1-sigma interval.
 A trajectory is created manually, imitated with a Cartesian DMP, converted
 to a joint trajectory by inverse kinematics, and executed with a UR5.
 
-[Script](examples/vis_cartesian_dmp.py)
+[Script](https://github.com/dfki-ric/movement_primitives/blob/main/examples/vis_cartesian_dmp.py)
 
 ### Contextual ProMPs
 
@@ -209,7 +152,7 @@ kinesthetic teaching. The panel width is considered to be the context over
 which we generalize with contextual ProMPs. Each color in the above
 visualizations corresponds to a ProMP for a different context.
 
-[Script](examples/external_dependencies/vis_contextual_promp_distribution.py)
+[Script](https://github.com/dfki-ric/movement_primitives/blob/main/examples/external_dependencies/vis_contextual_promp_distribution.py)
 
 **Dependencies that are not publicly available:**
 
@@ -229,7 +172,7 @@ visualizations corresponds to a ProMP for a different context.
 We offer specific dual Cartesian DMPs to control dual-arm robotic systems like
 humanoid robots.
 
-Scripts: [Open3D](examples/external_dependencies/vis_solar_panel.py), [PyBullet](examples/external_dependencies/sim_solar_panel.py)
+Scripts: [Open3D](https://github.com/dfki-ric/movement_primitives/blob/main/examples/external_dependencies/vis_solar_panel.py), [PyBullet](https://github.com/dfki-ric/movement_primitives/blob/main/examples/external_dependencies/sim_solar_panel.py)
 
 **Dependencies that are not publicly available:**
 
@@ -251,7 +194,7 @@ We can introduce a coupling term in a dual Cartesian DMP to constrain the
 relative position, orientation, or pose of two end-effectors of a dual-arm
 robot.
 
-Scripts: [Open3D](examples/external_dependencies/vis_cartesian_dual_dmp.py), [PyBullet](examples/external_dependencies/sim_cartesian_dual_dmp.py)
+Scripts: [Open3D](https://github.com/dfki-ric/movement_primitives/blob/main/examples/external_dependencies/vis_cartesian_dual_dmp.py), [PyBullet](https://github.com/dfki-ric/movement_primitives/blob/main/examples/external_dependencies/sim_cartesian_dual_dmp.py)
 
 **Dependencies that are not publicly available:**
 
@@ -271,7 +214,7 @@ Scripts: [Open3D](examples/external_dependencies/vis_cartesian_dual_dmp.py), [Py
 If we have a distribution over DMP parameters, we can propagate them to state
 space through an unscented transform.
 
-[Script](examples/external_dependencies/vis_dmp_to_state_variance.py)
+[Script](https://github.com/dfki-ric/movement_primitives/blob/main/examples/external_dependencies/vis_dmp_to_state_variance.py)
 
 **Dependencies that are not publicly available:**
 
@@ -286,6 +229,165 @@ space through an unscented transform.
   git clone git@git.hb.dfki.de:models-robots/kuka_lbr.git
   ```
 
+## Build API Documentation
+
+You can build an API documentation with sphinx.
+You can install all dependencies with
+
+```bash
+python -m pip install movement_primitives[doc]
+```
+
+... and build the documentation from the folder `doc/` with
+
+```bash
+make html
+```
+
+It will be located at `doc/build/html/index.html`.
+
+## Test
+
+To run the tests some python libraries are required:
+
+```bash
+python -m pip install -e .[test]
+```
+
+The tests are located in the folder `test/` and can be executed with:
+`python -m nose test`
+
+This command searches for all files with `test` and executes the functions with `test_*`.
+
+## Contributing
+
+You can report bugs in the [issue tracker](https://github.com/dfki-ric/movement_primitives/issues).
+If you have questions about the software, please use the [discussions
+section](https://github.com/dfki-ric/movement_primitives/discussions).
+To add new features, documentation, or fix bugs you can open a pull request
+on [GitHub](https://github.com/dfki-ric/movement_primitives). Directly pushing
+to the main branch is not allowed.
+
+The recommended workflow to add a new feature, add documentation, or fix a bug
+is the following:
+
+* Push your changes to a branch (e.g., feature/x, doc/y, or fix/z) of your fork
+  of the repository.
+* Open a pull request to the main branch of the main repository.
+
+This is a checklist for new features:
+
+- are there unit tests?
+- does it have docstrings?
+- is it included in the API documentation?
+- run flake8 and pylint
+- should it be part of the readme?
+- should it be included in any example script?
+
+## Non-public Extensions
+
+Scripts from the subfolder `examples/external_dependencies/` require access to
+git repositories (URDF files or optional dependencies) and datasets that are
+not publicly available. They are available on request (email
+alexander.fabisch@dfki.de).
+
+Note that the library does not have any non-public dependencies! They are only
+required to run all examples.
+
+### MoCap Library
+
+```bash
+# untested: pip install git+https://git.hb.dfki.de/dfki-interaction/mocap.git
+git clone git@git.hb.dfki.de:dfki-interaction/mocap.git
+cd mocap
+python -m pip install -e .
+cd ..
+```
+
+### Get URDFs
+
+```bash
+# RH5
+git clone git@git.hb.dfki.de:models-robots/rh5_models/pybullet-only-arms-urdf.git --recursive
+# RH5v2
+git clone git@git.hb.dfki.de:models-robots/rh5v2_models/pybullet-urdf.git --recursive
+# Kuka
+git clone git@git.hb.dfki.de:models-robots/kuka_lbr.git
+# Solar panel
+git clone git@git.hb.dfki.de:models-objects/solar_panels.git
+# RH5 Gripper
+git clone git@git.hb.dfki.de:motto/abstract-urdf-gripper.git --recursive
+```
+
+### Data
+
+I assume that your data is located in the folder `data/` in most scripts.
+You should put a symlink there to point to your actual data folder.
+
+## Related Publications
+
+This library implements several types of dynamical movement primitives and
+probabilistic movement primitives. These are described in detail in the
+following papers.
+
+[1] Ijspeert, A. J., Nakanishi, J., Hoffmann, H., Pastor, P., Schaal, S. (2013).
+    Dynamical Movement Primitives: Learning Attractor Models for Motor
+    Behaviors, Neural Computation 25 (2), 328-373. DOI: 10.1162/NECO_a_00393,
+    https://homes.cs.washington.edu/~todorov/courses/amath579/reading/DynamicPrimitives.pdf
+
+[2] Pastor, P., Hoffmann, H., Asfour, T., Schaal, S. (2009).
+    Learning and Generalization of Motor Skills by Learning from Demonstration.
+    In 2009 IEEE International Conference on Robotics and Automation,
+    (pp. 763-768). DOI: 10.1109/ROBOT.2009.5152385,
+    https://h2t.iar.kit.edu/pdf/Pastor2009.pdf
+
+[3] Muelling, K., Kober, J., Kroemer, O., Peters, J. (2013).
+    Learning to Select and Generalize Striking Movements in Robot Table Tennis.
+    International Journal of Robotics Research 32 (3), 263-279.
+    https://www.ias.informatik.tu-darmstadt.de/uploads/Publications/Muelling_IJRR_2013.pdf
+
+[4] Ude, A., Nemec, B., Petric, T., Murimoto, J. (2014).
+    Orientation in Cartesian space dynamic movement primitives.
+    In IEEE International Conference on Robotics and Automation (ICRA)
+    (pp. 2997-3004). DOI: 10.1109/ICRA.2014.6907291,
+    https://acat-project.eu/modules/BibtexModule/uploads/PDF/udenemecpetric2014.pdf
+
+[5] Gams, A., Nemec, B., Zlajpah, L., Wächter, M., Asfour, T., Ude, A. (2013).
+    Modulation of Motor Primitives using Force Feedback: Interaction with
+    the Environment and Bimanual Tasks (2013), In 2013 IEEE/RSJ International
+    Conference on Intelligent Robots and Systems (pp. 5629-5635). DOI:
+    10.1109/IROS.2013.6697172,
+    https://h2t.anthropomatik.kit.edu/pdf/Gams2013.pdf
+
+[6] Vidakovic, J., Jerbic, B., Sekoranja, B., Svaco, M., Suligoj, F. (2019).
+    Task Dependent Trajectory Learning from Multiple Demonstrations Using
+    Movement Primitives (2019),
+    In International Conference on Robotics in Alpe-Adria Danube Region (RAAD)
+    (pp. 275-282). DOI: 10.1007/978-3-030-19648-6_32,
+    https://link.springer.com/chapter/10.1007/978-3-030-19648-6_32
+
+[7] Paraschos, A., Daniel, C., Peters, J., Neumann, G. (2013).
+    Probabilistic movement primitives, In C.J. Burges and L. Bottou and M.
+    Welling and Z. Ghahramani and K.Q. Weinberger (Eds.), Advances in Neural
+    Information Processing Systems, 26,
+    https://papers.nips.cc/paper/2013/file/e53a0a2978c28872a4505bdb51db06dc-Paper.pdf
+
+[8] Maeda, G. J., Neumann, G., Ewerton, M., Lioutikov, R., Kroemer, O.,
+    Peters, J. (2017). Probabilistic movement primitives for coordination of
+    multiple human–robot collaborative tasks. Autonomous Robots, 41, 593-612.
+    DOI: 10.1007/s10514-016-9556-2,
+    https://link.springer.com/article/10.1007/s10514-016-9556-2
+
+[9] Paraschos, A., Daniel, C., Peters, J., Neumann, G. (2018).
+    Using probabilistic movement primitives in robotics. Autonomous Robots, 42,
+    529-551. DOI: 10.1007/s10514-017-9648-7,
+    https://www.ias.informatik.tu-darmstadt.de/uploads/Team/AlexandrosParaschos/promps_auro.pdf
+
+[10] Lazaric, A., Ghavamzadeh, M. (2010).
+     Bayesian Multi-Task Reinforcement Learning. In Proceedings of the 27th
+     International Conference on International Conference on Machine Learning
+     (ICML'10) (pp. 599-606). https://hal.inria.fr/inria-00475214/document
+
 ## Funding
 
 This library has been developed initially at the