A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation

2026-07-13 · Source: Artificial Intelligence · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning · Depth: Advanced, quick

Summary

REGRIND is a minimalist retargeting-guided reinforcement learning (RL) pipeline designed for dexterous manipulation. This system learns complex manipulation policies from a single human demonstration by first retargeting human hand-object motion to a robot reference, ensuring preservation of spatial and contact relationships. Subsequently, it trains a residual RL policy in simulation to track object-centric keypoints along this reference. The resulting policy is then transferred zero-shot to hardware, leveraging careful system identification. REGRIND has demonstrated fluid, human-like behavior on two distinct multi-fingered hands, successfully performing contact-rich tool-use tasks such as operating scissors and turning a screwdriver. The research also systematically analyzes key factors governing sim-to-real transfer in dexterous manipulation, providing practical guidance for retargeting-based learning in contact-rich environments.

Key takeaway

For Robotics Engineers developing dexterous manipulation policies, REGRIND offers a streamlined approach to achieve human-like behavior from minimal data. You should consider integrating retargeting human demonstrations with residual reinforcement learning to simplify policy acquisition. This method, combined with careful system identification, can enable zero-shot transfer to hardware, significantly reducing development time for contact-rich tasks like tool use.

Key insights

REGRIND uses retargeted human demos and residual RL for zero-shot sim-to-real dexterous manipulation.

Principles

• Retargeting preserves hand-object spatial/contact relationships.
• Residual RL tracks object-centric keypoints in simulation.
• Careful system identification enables zero-shot hardware transfer.

Method

REGRIND retargets human hand-object motion to a robot reference, trains a residual RL policy in simulation to track object-centric keypoints, then transfers zero-shot to hardware via system identification.

In practice

• Operate scissors with multi-fingered hands.
• Turn screwdrivers using dexterous robots.
• Apply retargeting for contact-rich tasks.

Topics

• REGRIND
• Dexterous Manipulation
• Reinforcement Learning
• Sim-to-Real Transfer
• Robot Retargeting
• Multi-fingered Robotics

Best for: Research Scientist, AI Scientist, Robotics Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.