RoboFind: Multi-Agent Personalized Object Search for People Who Are Blind or Have Low Vision

原文：RoboFind: Multi-Agent Personalized Object Search for People Who Are Blind or Have Low Vision

作者：Ruiping Liu, Shaofang Quan, Qian Yin, Jingqi Zhang, Junwei Zheng, Yufan Chen, Di Wen, Weijia Fan, Kailun Yang, M. Saquib Sarfraz, Tamim Asfour, Kunyu Peng, Rainer Stiefelhagen

来源：arXiv cs.RO（机器人）

正文

Computer Science > Robotics

arXiv:2609.20330v1 (cs)

[Submitted on 17 Sep 2026]

Title:RoboFind: Multi-Agent Personalized Object Search for People Who Are Blind or Have Low Vision

Authors:Ruiping Liu, Shaofang Quan, Qian Yin, Jingqi Zhang, Junwei Zheng, Yufan Chen, Di Wen, Weijia Fan, Kailun Yang, M. Saquib Sarfraz, Tamim Asfour, Kunyu Peng, Rainer Stiefelhagen

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Abstract:Blind and low-vision users often need to locate a specific personal object rather than an arbitrary instance of the same category. The task calls for a robot that can move through the space and reach viewpoints the user cannot, and for an accessible interface where the user says which object is meant and learns whether the right one was found. We present RoboFind, a multi-agent framework in which a smartphone teaches the target and a quadruped robot carries out the search. A Target Teaching Agent converts guided smartphone recordings into a semantic target profile and a reusable multi-view reference bank through an accessible capture flow with AR guidance, speech and haptic feedback, and screen-reader support, so later missions refer to a stored object without repeating the teaching process. At runtime, a Navigation Agent explores the environment and proposes candidate targets, a Verification Agent checks each candidate against the stored references, and a Coordination and Recovery Agent completes the mission or triggers recovery and continued search. Across 32 real-robot missions, RoboFind reaches 85.0% success against 25.0% for a reconstructed sequential first-stop baseline over 20 trials with ten targets, and reduces false success from 75.0% to 5.0%. On six shared targets it succeeds in 10/12 trials, against 5/12 for 12 independently executed GPT-6 Astra-only trials. These results show that the multi-agent design fits the demands of personalized object search, where verifying object identity before declaring completion is what makes the outcome something a user can rely on.

Subjects:

Robotics (cs.RO)

Cite as:

arXiv:2609.20330 [cs.RO]

(or

arXiv:2609.20330v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.20330

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机器人

由「前沿雷达」于 2026-09-20 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。