To Run Z1 Manipulator Pick-and-Place Object Simulation

1. Download the required assets:

   • Download the YCB and Sektion_Cabinet folders from the shared Google Drive link and place them in your Downloads folder.
   • Download the logs folder and place it inside the IsaacLab directory.

2. Ensure dynamic user directory access:

   • Ensure that the code accesses the correct user directory dynamically by using environment variables or Python's dynamic directory handling (e.g., os.path.expanduser("~")).

3. To pick up the cube:

   • Uncomment line 73 in joint_pos_env_cfg.py where the usd_path is set to:

     usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/DexCube/dex_cube_instanceable.usd"

   • The joint_pos_env_cfg.py file is located in the following directory:

     ~/IsaacLab/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/manager_based/manipulation/z1_lift_from_drawer/config/z1

   • Then, run the following command to start the simulation:

     ./isaaclab.sh -p source/standalone/workflows/rsl_rl/play.py --task Isaac-Lift-Cube-from-drawer-Z1-v0 --num_envs 16 --load_run 2024-09-20_16-45-10_cube_1500

4. To pick up the mustard bottle:

   • Uncomment line 72 in joint_pos_env_cfg.py where the usd_path is set to:

     usd_path=os.path.join(os.path.expanduser("~"), "Downloads/YCB/Axis_Aligned/006_mustard_bottle.usd")

   • The joint_pos_env_cfg.py file is located in the following directory:

     ~/IsaacLab/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/manager_based/manipulation/z1_lift_from_drawer/config/z1

   • Then, run the following command to start the simulation:

     ./isaaclab.sh -p source/standalone/workflows/rsl_rl/play.py --task Isaac-Lift-Cube-from-drawer-Z1-v0 --num_envs 16 --load_run 2024-09-20_18-15-07_mustard

Notes:

• Make sure that you uncomment the correct lines corresponding to the object you want the manipulator to pick up.
• The simulation will load with the respective object (cube or mustard bottle) based on the configuration.

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Isaac Lab

Isaac Lab is a unified and modular framework for robot learning that aims to simplify common workflows in robotics research (such as RL, learning from demonstrations, and motion planning). It is built upon NVIDIA Isaac Sim to leverage the latest simulation capabilities for photo-realistic scenes and fast and accurate simulation.

Please refer to our documentation page to learn more about the installation steps, features, tutorials, and how to set up your project with Isaac Lab.

Contributing to Isaac Lab

We wholeheartedly welcome contributions from the community to make this framework mature and useful for everyone. These may happen as bug reports, feature requests, or code contributions. For details, please check our contribution guidelines.

Troubleshooting

Please see the troubleshooting section for common fixes or submit an issue.

For issues related to Isaac Sim, we recommend checking its documentation or opening a question on its forums.

Support

• Please use GitHub Discussions for discussing ideas, asking questions, and requests for new features.
• Github Issues should only be used to track executable pieces of work with a definite scope and a clear deliverable. These can be fixing bugs, documentation issues, new features, or general updates.

License

The Isaac Lab framework is released under BSD-3 License. The license files of its dependencies and assets are present in the docs/licenses directory.

Acknowledgement

Isaac Lab development initiated from the Orbit framework. We would appreciate if you would cite it in academic publications as well:

@article{mittal2023orbit,
   author={Mittal, Mayank and Yu, Calvin and Yu, Qinxi and Liu, Jingzhou and Rudin, Nikita and Hoeller, David and Yuan, Jia Lin and Singh, Ritvik and Guo, Yunrong and Mazhar, Hammad and Mandlekar, Ajay and Babich, Buck and State, Gavriel and Hutter, Marco and Garg, Animesh},
   journal={IEEE Robotics and Automation Letters},
   title={Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments},
   year={2023},
   volume={8},
   number={6},
   pages={3740-3747},
   doi={10.1109/LRA.2023.3270034}
}

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