PKU-MARL/DexterousHands

Health v2 · maintenance only

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Adoption not part of the score

1085 stars · 134 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Bi-DexHands is a Python library providing bimanual dexterous hand manipulation environments built on NVIDIA Isaac Gym for reinforcement learning research. It includes a comprehensive benchmark of RL, MARL, multi-task RL, meta-RL, and offline RL algorithms across tasks like handover, throw, and lift with thousands of YCB/SAPIEN objects.

Use cases

• train RL agents on bimanual dexterous hand manipulation
• benchmark multi-agent RL algorithms on robotic hand tasks
• run thousands of parallel physics simulations on a single GPU
• study task generalization with meta-RL across manipulation tasks
• simulate two-handed object handover and throw tasks
• compare robot skill learning against human motor development

When to choose

• you need GPU-accelerated parallel simulation of dual dexterous hands
• you research multi-agent or multi-task RL for bimanual manipulation
• you want a standardized benchmark with diverse objects and tasks

When to avoid

• you need real hardware robot control rather than simulation
• you require single-arm or non-dexterous manipulation environments
• you cannot use NVIDIA Isaac Gym or an NVIDIA GPU

Facets

library · maturity active

reinforcement-learning simulation robotics benchmarking reinforcement-learning robotics simulation machine-learning python isaac-gym dexterous-manipulation bimanual-robotics marl gym-environments sim2real gpu linux

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