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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 skip segment, we replace the behavior cloning target with the action at the entrance of the next key segment, enabling the policy to leap over redundant steps in a single decision. The resulting \\textbf{Skip Policy (SkiP)} dynamically leaps over skip segments and intensively refines actions in key segments, within a single unified network requiring no learned skip planner or hierarchical structure. To automatically partition demonstrations into k","title":"SkiP: When to Skip and When to Refine for Efficient Robot Manipulation","url":"https://arxiv.org/abs/2605.15536","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.15536v1 Announce Type: cross \nAbstract: 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 skip segment, we replace the behavior cloning target with the action at the entrance of the next key segment, enabling the policy to leap over redundant steps in a single decision. The resulting \\textbf{Skip Policy (SkiP)} dynamically leaps over skip segments and intensively refines actions in key segments, within a single unified network requiring no learned skip planner or hierarchical structure. 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