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

Autor:	Gallouédec, Quentin, Cazin, Nicolas, Dellandréa, Emmanuel, Chen, Liming
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: NeurIPS 2021 Workshop on Robot Learning: Self-Supervised and Lifelong Learning
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2106.13687 Zobrazit plný text záznamu View this record from Arxiv