Fast and Reliable Open-Vocabulary Object Search with Absence Detection from Robot Memory

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为 VLA、具身智能、人形机器人控制、机器人操作等高价值论文补充中文说明。

Original abstract

Mobile manipulators deployed in everyday human environments, such as homes for assistive care or flexible automation, often need to locate specific objects on demand from accumulated observations. Equally important, they must reliably determine when a target is absent before committing to navigation or interaction. Existing approaches demand either slow exhaustive re-processing or expensive 3D representations, and are evaluated on recall and accuracy rather than specificity, leaving their ability to abstain on missing targets unmeasured. We find existing systems that search using text or visual embedding almost never abstain on absent objects achieving at most 9\% specificity. To support accurate search at speed, we present a two-stage architecture that decouples fast candidate retrieval from verified selection, enabling both high recall and reliable absence detection from sequential RGB observations with pose. We evaluate eight retrieval-selection configurations plus an exhaustive search baseline across ScanNet apartment scenes, including significant numbers of absent objects. Adding a visual verification step to caption-based retrieval matches or exceeds exhaustive search reliability while requiring an order of magnitude fewer detector calls. We reproduce ScanNet scores on a physical robot operating entirely on-board with a single consumer GPU running in real time.

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• Official / arXiv page
• PDF from original source

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