This repository provides a suite of robotic manipulation environments built on the PyBullet physics engine and the Gymnasium API. It is designed for researchers and practitioners in reinforcement learning and robotics, offering goal-conditioned tasks that facilitate the development and benchmarking of robotic control algorithms.

How It Works

The environments are implemented using the PyBullet physics simulator, providing realistic physics and rendering capabilities. They are integrated with the Gymnasium API, ensuring compatibility with standard RL training pipelines. The environments are goal-conditioned, meaning the agent receives a goal state as part of the observation, enabling the training of more generalizable policies.

Quick Start & Requirements

• Primary install: pip install panda-gym
• Prerequisites: Python 3.7+, PyBullet, Gymnasium.
• Documentation: https://qgallouedec.github.io/panda-gym/

Highlighted Details

• Offers six goal-conditioned robotic manipulation environments: Reach, Push, Slide, PickAndPlace, Stack, and Flip.
• Inspired by OpenAI Fetch environments, providing a familiar interface for those transitioning from previous benchmarks.
• Baselines results and pre-trained agents are available via rl-baselines3-zoo and Hugging Face Hub.

Maintenance & Community

• The project was presented at the 4th Robot Learning Workshop at NeurIPS 2021.
• No explicit community links (Discord/Slack) or roadmap are provided in the README.

Licensing & Compatibility

• The README does not explicitly state a license. Based on typical open-source practices for research projects, it is likely permissive (e.g., MIT or Apache), but this requires verification.
• Compatibility with commercial or closed-source projects is not specified.

Limitations & Caveats

The README does not detail specific limitations, performance benchmarks, or known issues. The absence of an explicit license requires due diligence for commercial use.