panda-gym: Open-source goal-conditioned environments for robotic learning
This provides a standardized toolkit for robotic learning research, though it is incremental as it builds on existing frameworks like OpenAI Gym and PyBullet.
The paper introduces panda-gym, an open-source set of goal-conditioned reinforcement learning environments for the Franka Emika Panda robot, including five tasks like reach and stack, and provides baseline results using state-of-the-art model-free off-policy algorithms.
This paper presents panda-gym, a set of Reinforcement Learning (RL) environments for the Franka Emika Panda robot integrated with OpenAI Gym. Five tasks are included: reach, push, slide, pick & place and stack. They all follow a Multi-Goal RL framework, allowing to use goal-oriented RL algorithms. To foster open-research, we chose to use the open-source physics engine PyBullet. The implementation chosen for this package allows to define very easily new tasks or new robots. This paper also presents a baseline of results obtained with state-of-the-art model-free off-policy algorithms. panda-gym is open-source and freely available at https://github.com/qgallouedec/panda-gym.