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KiRoSe: Efficiently Segmenting Robot Demonstrations with Kinematic Data

KiRoSe proposes segmenting demonstration data using robot kinematic data rather than multi-camera video streams, compactly representing recurring motion events through Sparse Motion Tokens (SMTs). The method supervises an open language model with subtask annotations to interpret token sequences, clearly outperforming baseline segmentation methods on the F1 metric, and can transfer zero-shot to unseen robot embodiments and first-person human motion. Beyond segmentation, KiRoSe can also track task progress and detect action reversals on unseen robots.

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