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CLOSER: Evidence-Aware Active Visual Verification for Embodied Object-Goal Navigation

Pengying Wu, Mingye Jiang, Yunze Hu, Zhejia Wen, Kangjie Zhou, Zongru Yang, Zhe Peng, Chang Liu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28476 · cited by 0 works (OpenAlex) · open-access PDF

Korean

오브젝트 탐지기 (ObjectNav)는 특정 범주에서 지정된 범주의 인스턴스를 찾고 보이지 않는 환경에서 중지하기 위해 구체화된 에이전트를 필요로합니다. 에이전트는 일반적으로 개체 탐지기에 의존하지만 제한된 자아 중심 비디오 스트림, 때로는 단일 프레임에서 온라인으로 판단해야합니다. 탐지기들은 종종 침대에 대한 소파와 같은 유사한 개체를 자신있게 오해하고 가짜 긍정적 인 에피소드에 중지하면 에피소드가 실패합니다. 지배적 인 시스템은 또한 열광적으로 후보자에게 접근하여 그들을 차단하거나 부분적으로 관찰해야합니다. 우리는 CLOSER를 제시합니다, 증거를 인식하는 활발한 검증 프레임으로

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

Abstract

Object-goal navigation (ObjectNav) requires an embodied agent to find an instance of a specified category in an unseen environment and stop near it. The agent typically relies on an object detector but must judge online from a limited egocentric video stream, sometimes a single frame, whether a detected candidate is the target. Detectors often confidently mistake similar objects, such as a sofa for a bed, and stopping at a false positive fails the episode. Prevailing systems also approach candidates greedily, leaving them occluded or partially observed. We present CLOSER, an evidence-aware active verification framework that treats stopping as closed-loop evidence acquisition, coupling vision-language model (VLM) verification with active perception. CLOSER fine-tunes an open-source VLM to label each view of a candidate tracked across frames as target, non-target, or uncertain, selects complementary views with an embedding-based diversity gate, and fuses their per-view probabilities with a lightweight network. If the fused judgment remains uncertain, a diffusion policy drives the agent toward the candidate using only the tracked mask and depth until the decision becomes decisive. CLOSER runs locally and plugs into existing ObjectNav systems without retraining them. On Gibson and Habitat-Matterport 3D, it raises average success rate by 3.6 points and success weighted by path length by 2.0 points, showing that verifying evidence before committing improves embodied navigation.

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Topics: Computer science, Embodied cognition, Mistake, Artificial intelligence, Computer vision, Object (grammar)

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