A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation
Recent work in humanoid whole-body control has found success with a simple recipe: retarget human motion to robot kinematic references, then train policies via reinforcement learning (RL) to track them. But how does this recipe transfer to dexterous manipulation? The answer is not obvious, as manipulation involves complex, contact-rich dynamics and requires delicate regulation of contact modes and forces. We present REGRIND, a minimalist retargeting-guided RL pipeline that learns dexterous manipulation policies from a single human demonstration. REGRIND retargets human hand-object motion to a
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- PossiblePossibly related (embedding) · 60%UK startup Humanoid launches reinforcement learning system to improve robot manipulation - Robotics & Automation News →
- PossiblePossibly related (embedding) · 49%Gradient-based Planning for World Models at Longer Horizons →
- PossiblePossibly related (embedding) · 49%AgileRL/AgileRL →
- PossiblePossibly related (embedding) · 47%Humanoid says KinetIQ Ascend reinforcement learning approaches human-level dexterity - The Robot Report →
- PossiblePossibly related (embedding) · 47%pytorch/rl →
- LinkedLinked via arxiv author · 85%Yunhai Feng →
“A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation”
- LinkedLinked via arxiv author · 85%Natalie Leung →
“A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation”
- LinkedLinked via arxiv author · 85%Jiaxuan Wang →
“A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation”