Hand-centric Human-to-Robot Trajectory Transfer from Video Demonstrations via Open-World Contact Localization

• URL: http://arxiv.org/abs/2606.10743v1
• Date: Tue, 09 Jun 2026 11:53:29 GMT
• Title: Hand-centric Human-to-Robot Trajectory Transfer from Video Demonstrations via Open-World Contact Localization
• Abstract summary: We present emphHOWTransfer, a hand-centric framework that distills human demonstrations into contact-aware, taxonomy-informed, and diverse robotic trajectories.<n>emphHOWTransfer recovers temporally consistent 3D hand motion and localizes temporal contact intervals by reasoning over observed hand-object interaction cues.<n>Experiments show that emphHOWTransfer enables accurate contact localization and high-quality robot motion with $86%$ success.
• Score: 28.23926683554352
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning from human video demonstrations remains challenging due to noisy hand-object interactions, unseen objects with partial observation, and cross-embodiment discrepancy. To address these challenges, we present \textit{HOWTransfer} (\emph{H}and-\emph{O}bject \emph{O}pen-\emph{W}orld Transfer), a hand-centric framework that distills human demonstrations into contact-aware, taxonomy-informed, and diverse robotic trajectories. Instead of relying on object-specific descriptions, vision-language queries, or explicit object-state tracking, \emph{HOWTransfer} recovers temporally consistent 3D hand motion and localizes temporal contact intervals by reasoning over observed hand-object interaction cues. The localized contact onsets are then used to retarget human grasp intent into multi-modal parallel-jaw grasp hypotheses, which are propagated along the recovered wrist trajectory to generate robot-executable motions. Finally, a trajectory editing stage refines contact alignment and produces diverse executable variants from a single demonstration. Experiments across diverse manipulation tasks show that \emph{HOWTransfer} enables accurate contact localization and high-quality robot motion retargeting with $86\%$ success, which is preferred over teleoperated trajectories in a blinded preference study.

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