SkiP: When to Skip and When to Refine for Efficient Robot Manipulation

Previous imitation learning policies predict future actions at every control step, whether in smooth motion phases or precise, contact-rich operation phases. This uniform treatment is wasteful: most steps in a manipulation trajectory traverse free space and carry little task-relevant information, while a small fraction of \emph{key} steps around contacts, grasps, and alignment demand dense, high-resolution prediction. We propose a novel \emph{action relabeling} mechanism: at each timestep in a s

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ethics.ai (15 May 2026), “SkiP: When to Skip and When to Refine for Efficient Robot Manipulation,” evidence record 4288, https://ethics.ai/record/4288 (originally published by arXiv).

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