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This paper presents panda-gym, a set of Reinforcement Learning (RL)environments for the Franka Emika Panda robot integrated with OpenAI Gym. Fivetasks are included: reach, push, slide, pick & place and stack. They all followa Multi-Goal RL framework, allowing to use goal-oriented RL algorithms. Tofoster open-research, we chose to use the open-source physics engine PyBullet.The implementation chosen for this package allows to define very easily newtasks or new robots. This paper also presents a baseline of results obtainedwith state-of-the-art model-free off-policy algorithms. panda-gym isopen-source and freely available at https://github.com/qgallouedec/panda-gym.

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