This repository contains the official implementation of the paper Constrained Reinforcement Learning for Unstable Point-Feet Bipedal Locomotion Applied to the Bolt Robot by Constant Roux, Elliot Chane-Sane, Ludovic de Matteïs, Thomas Flayols, Jérôme Manhes, Olivier Stasse and Philippe Souères.

This paper has been accepted for the 2025 IEEE-RAS 24rd International Conference on Humanoid Robots (Humanoids).

• Install Isaac Lab 2.x.x by following the installation guide.
• Clone the repository separately from the Isaac Lab installation (i.e., outside the IsaacLab directory).
• Using a Python interpreter that has Isaac Lab installed, install the library:

python -m pip install -e exts/cat_envs

Navigate to the Bolt-CaT-RL directory and launch a training:

python scripts/clean_rl/train.py --task=Isaac-Velocity-CaT-Bolt-v0 --headless

If everything goes well, you will see monitoring in the terminal as the training progresses. At the end, you can check the result with:

python scripts/clean_rl/play.py --task=Isaac-Velocity-CaT-Bolt-Play-v0 --headless --video --video_length 200

If you find this project useful for your work please cite:

@inproceedings{roux2025bolt,
      title={Constrained Reinforcement Learning for Unstable Point-Feet Bipedal Locomotion Applied to the Bolt Robot},
      author={Constant Roux and Elliot Chane-Sane and Ludovic de Matteïs and Thomas Flayols and Jérôme Manhes and Olivier Stasse and Philippe Souères and Nicolas Mansard},
      booktitle={2025 IEEE-RAS 24rd International Conference on Humanoid Robots (Humanoids)}, 
      year={2025}
}