Open-Source Reinforcement Learning Environments Implemented in MuJoCo with Franka Manipulator
CoRR(2023)
摘要
This paper presents three open-source reinforcement learning environments
developed on the MuJoCo physics engine with the Franka Emika Panda arm in
MuJoCo Menagerie. Three representative tasks, push, slide, and pick-and-place,
are implemented through the Gymnasium Robotics API, which inherits from the
core of Gymnasium. Both the sparse binary and dense rewards are supported, and
the observation space contains the keys of desired and achieved goals to follow
the Multi-Goal Reinforcement Learning framework. Three different off-policy
algorithms are used to validate the simulation attributes to ensure the
fidelity of all tasks, and benchmark results are also given. Each environment
and task are defined in a clean way, and the main parameters for modifying the
environment are preserved to reflect the main difference. The repository,
including all environments, is available at
https://github.com/zichunxx/panda_mujoco_gym.
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