Poster
in
Workshop: 4th Robot Learning Workshop: Self-Supervised and Lifelong Learning

panda-gym : Open-source goal-conditioned enviroments for robotic learning

Quentin Gallou├ędec · Emmanuel Dellandrea · Liming Chen

Abstract:

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 [anonymized link].

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