{"paper":{"title":"SkiP: When to Skip and When to Refine for Efficient Robot Manipulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"SkiP lets a single robot policy learn to skip low-information steps by relabeling actions to the next key segment.","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.RO","authors_text":"Chunjie Chen, Feng Yan, Guanqi Peng, Liang Lin, Lingbo Liu, Mingtong Dai, Xinyu Wu, Yongjie Bai","submitted_at":"2026-05-15T02:16:34Z","abstract_excerpt":"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 "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The resulting 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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That replacing the behavior cloning target at each timestep in a skip segment with the action at the entrance of the next key segment still produces a policy that can be executed safely and successfully in closed-loop control without additional safety mechanisms or recovery behaviors.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"SkiP introduces action relabeling and Motion Spectrum Keying to skip redundant steps in robot trajectories, cutting executed steps by 15-40% while maintaining success rates across 72 simulated and 3 real tasks.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"SkiP lets a single robot policy learn to skip low-information steps by relabeling actions to the next key 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