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SPAR: Search-trained Push Action Ranker for Efficient Navigation Among Movable Obstacles

Dhruv Metha Ramesh, Tri-An Nguyen, Junshan Huang, Jingjin Yu, Kostas E. Bekris, Abdeslam Boularias · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28362 · cited by 0 works (OpenAlex) · open-access PDF

Korean

SP27 Movable Obstacles (NAMO)는 로봇이 목표에 접근을 차단하는 객체를 재배치하도록 요구합니다. pushing 로봇의 경우, 진보는 시뮬레이션 검색 결과에서 프리미티브를 프리미티브로 설정하지 않고 나중에 개방할 수 있습니다. candidate 시퀀스의 수는 시퀀스 길이와 함께 배열을 증가시킬 수 있으며, 물리학 기반 검색을 비용이 부담스럽게 만들 수 있습니다. Search-trained Push Action Ranker (SPAR)는 시뮬레이션 검색 결과에서 프리미티브를 프리미티브로 설정하는 것을 배우는 Search-trained Push Action Ranker를 도입합니다. Search-trained transformator scores primitives using contact tokens on geometry and reachability within 21 regions. The same model

Machine translation (Meta M2M100); the English original follows.

Abstract

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 setup pushes. A transformer trained with regression and ranking losses scores primitives using contact tokens conditioned on scene geometry and reachability regions. The same model guides simulator evaluations in SPAR Search and selects actions for direct execution in SPAR Policy. Both modes solve successive keyhole problems that open access to neighboring free-space regions within a NAMO planner. Across successful runs on $300$ simulated two-push keyhole problems, SPAR Search reduces median simulator calls by $7.0\times$, leading to a $3.8\times$ reduction in planning time relative to Random Search. Without retraining, the same ranker guides search on $600$ keyhole problems which have no solution within two pushes. At a depth limit of five pushes, SPAR Search achieves $50.7\%$ success within $100$ simulator calls, compared with $20.2\%$ for Random Search. These gains carry over to NAMO planning: on hard simulated scenes, SPAR Search achieves $47\%$ success within $100$ simulator calls, compared with $21\%$ for Random Search. In real-robot experiments, SPAR Policy succeeds in $37/40$ trials without a physics-based digital twin, compared with $33/40$ for SPAR Search and $27/40$ for Random Search.

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Topics: Spar, Computer science, Simulation, Random search, Artificial intelligence, Computer vision

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange