Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning. (arXiv:2206.08686v1 [cs.RO])

Web: http://arxiv.org/abs/2206.08686

Achieving human-level dexterity is an important open problem in robotics.
However, tasks of dexterous hand manipulation, even at the baby level, are
challenging to solve through reinforcement learning (RL). The difficulty lies
in the high degrees of freedom and the required cooperation among heterogeneous
agents (e.g., joints of fingers). In this study, we propose the Bimanual
Dexterous Hands Benchmark (Bi-DexHands), a simulator that involves two
dexterous hands with tens of bimanual manipulation tasks and thousands of
target objects. Specifically, tasks …

arxiv human learning reinforcement reinforcement learning