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PARC: Physics-based Augmentation with Reinforcement Learning for Character Controllers
| Date | Stars |
|---|---|
| 2026-07-31 | 341 |
| 2026-08-01 | 341 |
| 2026-08-02 | 341 |
| 2026-08-04 | 342 |
| 2026-08-06 | 342 |
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# PARC  Project page: https://michaelx.io/parc # Installation Tested with Ubuntu 22.04 **Optional**: Install Isaac Gym (https://developer.nvidia.com/isaac-gym) for motion tracking Install requirements: ``` conda create -n parc python=3.8.20 conda activate parc pip install -r requirements.txt ``` and it should be good to go. If pytorch is not being able to detect CUDA, try reinstalling: ``` pip install torch==2.2.0+cu118 -f https://download.pytorch.org/whl/torch_stable.html ``` You can then run scripts such as: ``` python scripts/run_motionscope.py ``` ## Motionscope Motionscope is my motion/terrain editor, as well as motion generator visualizer, built on top of Polyscope: https://polyscope.run/py/. You can run it by running: ``` python scripts/run_motionscope.py ``` Before that, you must edit the config file `parc/motionscope/motionscope_config.yaml` to load the motion you want, as well as optionally load an MDM model. ## Dataset and Models Download the datasets from the initial iteration and each stage of PARC. ### New release: https://huggingface.co/datasets/mxucg/PARC Two separate PARC experiments (Dec 2024 with 4 iterations, April 2025 with 5 iterations). New small model (~30 mb). These files are loaded with anim/motion_lib.py and anim/kin_char_model.py. You can view them with scripts/run_motionscope.py, by editing the "motion_filepath" param in parc/motionscope/motionscope_config.yaml If you only want the data without installing the whole repo, check out the script: scripts/read_motion_data.py You should only need numpy (and maybe pytorch?) to read the data. (Old release: https://1sfu-my.sharepoint.com/:f:/g/personal/mxa23_sfu_ca/Et16uLMFxoRKouibvBa7LbwBEmX5_iI5a8dZyiMc0wmSTA?e=ihma1b The password is "PARC". The file format is only compatible with v0.1 PARC release.) ## User configuration All configuration files reference data, checkpoints, and generated outputs through a `$DATA_DIR` placeholder. Set this base directory in `user_config.yaml` at the repository root: ``` DATA_DIR: "/absolute/path/to/your/data" ``` `DATA_DIR` must be an existing absolute path. The training and pipeline scripts will automatically replace `$DATA_DIR` in YAML configs with the configured value when they load them. ## Motion Tracking PARC's motion tracking module was written using Isaac Gym, based on an early version of MimicKit. Isaac Gym is deprecated and there are now many great open-source motion tracking repositories. I personally recommend using https://github.com/xbpeng/MimicKit The recipe for building a blocky terrain style motion tracking environment as shown in PARC is to start from a DeepMimic environment, but to load in a terrain that is composed of all the terrain-motion pairs in your dataset laid out as a grid. The environment should keep track of which motion-terrain each agent is currently learning, book-keeping the position offset for the agent and reference motions. If you still wish to use Isaac Gym, then install it here: https://developer.nvidia.com/isaac-gym I recommend using their installation scrip to install it within a **conda** environment with python 3.8.20 (Other versions may also work, but not tested). The isaac gym helper yaml should look like this: ``` name: parc channels: - pytorch - conda-forge - defaults dependencies: - python=3.8.20 - pytorch=2.20.0 - torchvision=0.9.1 - cudatoolkit=11.1 - pyyaml>=5.3.1 - scipy>=1.5.0 - tensorboard>=2.2.1 ``` ## Codebase Guide * [PARC Guide](doc/parc_guide.md) ## Citation If you find PARC helpful, please consider citing the references in the [citation document](./doc/cite.md).
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matched fp:67bd448e6c9baa48, llm:Repository title and description: 'PARC: Physics-based Augmentation with Reinforcement Learning for Character Controllers' (Python).
matched fp:67bd448e6c9baa48, llm:Repository title and description: 'PARC: Physics-based Augmentation with Reinforcement Learning for Character Controllers' (Python).
matched fp:67bd448e6c9baa48, llm:Repository title and description: 'PARC: Physics-based Augmentation with Reinforcement Learning for Character Controllers' (Python).