REGRIND: A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation
Score
78该论文首个系统性地将重定向+强化学习配方从人形全身控制迁移至灵巧操作，并揭示关键迁移因素，信息增量较高。
实验设计严谨，包含多种任务和硬件对比，理论分析扎实，代码开源，质量上乘。
提供的极简方案和迁移指南可直接指导机器人灵巧操作的实际部署，实用价值明显。
2025年最新成果，紧跟灵巧操作与强化学习前沿，时效性极强。
论文来自arXiv预印本，作者团队有扎实机器人背景，代码开源可供复现，可信度高。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The method is novel with solid experiments, providing a clean baseline for dexterous manipulation RL, though more tasks and hardware generalization need verification.
AI commentary
「This paper brings the successful 'retargeting+RL' recipe from humanoid whole-body control to dexterous manipulation, training transferable policies from a single human demo—great for both labs and real-world deployment. If you're struggling with sim-to-real gaps, the system identification and domain randomization analysis here offer practical takeaways.」
Quick overview
A new arXiv paper presents REGRIND, a minimalist pipeline that learns dexterous manipulation policies from a single human demonstration. It retargets human hand-object motion to robot kinematic references preserving spatial and contact relationships, trains a residual RL policy in simulation to track object-centric keypoints, and transfers zero-shot to hardware with careful system identification. The resulting policies produce fluid, human-like behavior on multi-fingered hands for contact-rich tool-use tasks like scissors and screwdriver. Systematic hardware experiments identify key factors governing sim-to-real transfer.
- Proposes REGRIND, a minimalist pipeline learning dexterous manipulation from a single human demo.
- Uses retargeting to preserve hand-object spatial/contact relations, with residual RL and system ID for zero-shot transfer.
- Validated on multi-fingered hands for contact-rich tasks (scissors, screwdriver), achieving human-like fluidity.
Full text
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