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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

Hindi

Object-target navigation (ObjectNav) को एक परिभाषित एजेंट को एक विशिष्ट श्रेणी के एक उदाहरण को एक अदृश्य वातावरण में ढूंढने और उसके पास रुकने के लिए आवश्यक है। एजेंट आमतौर पर एक वस्तु डिटेक्टर पर भरोसा करता है, लेकिन एक सीमित स्वार्थी वीडियो स्ट्रीम से ऑनलाइन निर्णय करना पड़ता है, कभी-कभी एक ही फ्रेम, चाहे एक परिभाषित उम्मीदवार लक्ष्य है। डिटेक्टर अक्सर एक बिस्तर के लिए एक सोफे जैसे समान वस्तुओं को भरोसेमंद रूप से गलती करते हैं, और एक झूठी सकारात्मक एजेंट पर रोकते हुए एपिसोड में असफल होता है। प्रचलित सिस्टम भी उम्मीदवारों को उदारता से संपर्क करते हैं, उन्हें अवरुद्ध या

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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