Dhruv Metha Ramesh, Tri-An Nguyen, Junshan Huang, Jingjin Yu, Kostas E. Bekris, Abdeslam Boularias · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28362 · open-access PDF
移动障碍之中的导航(NAMO)要求机器人重新安排阻止其目标访问的对象。对于推动机器人来说,进展可能取决于一个设置推动,使后续打开无需自行清除一个通道。候选序列的数量可以随着序列长度增长,使物理基于的搜索成本高昂。我们引入了经过搜索训练的推动行动排名(SPAR),该系统可以从模拟搜索结果中优先考虑推动原始人。
Machine translation (Meta M2M100); the English original follows.
Navigation Among Movable Obstacles (NAMO) requires a robot to rearrange objects that block access to its goal. For a pushing robot, progress may depend on a setup push that enables a later opening without itself clearing a passage. The number of candidate sequences can grow exponentially with sequence length, making physics-based search costly. We introduce the Search-trained Push Action Ranker (SPAR), which learns to prioritize push primitives from simulated search outcomes. Searches over sequences of up to two pushes per object provide supervision that distinguishes immediate openings from s
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