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RoboFind: Multi-Agent Personalized Object Search for People Who Are Blind or Have Low Vision — arXiv:2609.20330

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.20330
License (as archived): CC0-1.0
Archived: 2026-10-10T05:25:42.903Z
SHA-256 of the archived text: 601cc1dff6bacd6846a7491662653e12a2c4bae1c1e8bad89efb4309d080f894
Stable URL of this page: https://metraipolis.com/documento/dc_63de35eae4da

How to cite

RoboFind: Multi-Agent Personalized Object Search for People Who Are Blind or Have Low Vision — arXiv:2609.20330 — arXiv (en), https://arxiv.org/abs/2609.20330 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_63de35eae4da (Metraipolis archive dc_63de35eae4da, sha256 601cc1dff6bacd6846a7491662653e12a2c4bae1c1e8bad89efb4309d080f894)

BibTeX

@misc{dc63de35eae4da,
  title = {RoboFind: Multi-Agent Personalized Object Search for People Who Are Blind or Have Low Vision — arXiv:2609.20330},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.20330},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_63de35eae4da; sha256 601cc1dff6bacd6846a7491662653e12a2c4bae1c1e8bad89efb4309d080f894},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.20330. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-18 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.20330 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-10T04:47:38.895Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).