REGRIND is a minimal retargeting-guided reinforcement learning framework for dexterous manipulation, implemented in IsaacLab and IsaacSim.

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License

MIT

Last updated

2026-07-14

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FR-AI / ANALYSIS

Why it is worth attention

It offers a streamlined, reproducible recipe for learning dexterous manipulation skills by combining motion retargeting from human hand trajectories with RL, including prepared retargeted datasets and a gravity curriculum.

Who it is for

  • Robotics researchers studying dexterous manipulation
  • Reinforcement learning practitioners working on sim-to-real transfer
  • Developers building on IsaacLab/IsaacSim for manipulation tasks

Use cases

  • Training dexterous policies for hand-object interaction (e.g., scissors, screwdriver)
  • Benchmarking retargeting and RL pipelines on standardized tasks
  • Evaluating policy generalization through play environments and gravity curriculum

Strengths

  • Provides precomputed retargeted trajectories so users can skip retargeting
  • Modular retargeting with Drake and optional Mosek/Clarabel solvers
  • Clear training and evaluation scripts with wandb logging and video recording

Considerations

  • Requires specific versions of IsaacSim (5.1.0) and IsaacLab (2.3.0)
  • Retargeting depends on Drake and optionally Mosek (license needed for Mosek)
  • Limited to two robot hands (LeapHand, WujiHand) and two objects (scissors, screwdriver)

README quick start

Installation

Description

AMinimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation

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