Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning

Understand

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.