REGRIND

REGRIND: Minimalist retargeting-guided RL pipeline for dexterous manipulation from single human demo

Researchers introduce REGRIND, a pipeline that learns dexterous robot manipulation policies from a single human demonstration by retargeting hand-object motion to robot kinematic references and training a residual RL policy to track object-centric keypoints. The system achieves zero-shot sim-to-real transfer on multi-fingered hands for contact-rich tasks including scissors operation and screwdriver turning. The paper systematically analyzes key factors governing sim-to-real transfer in dexterous manipulation, extending a recipe previously validated for humanoid whole-body control into the harder contact-rich manipulation domain.