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Retiming Robot Demonstrations with a Learned Speed Prior

The study proposes learning a speed prior from large-scale demonstration data: given a task instruction, a motion path, and images along the path, it predicts a distribution of execution speed along the path and uses a selected quantile as a unified clock to retime each demonstration. The method fits the speed prior on robot teleoperation data and on human handheld-gripper demonstrations, retimes robot demonstrations to the handheld pace, and then fine-tunes a vision-language-action policy; at execution, a real-time optimal-control layer tracks the predicted action chunks under robot constraints.

Part of the event: Learning a Speed Prior for Robot Demonstration Retiming

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