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arXiv · 2608.18292

GuideFetch: A Task Coordination Framework for Concurrent Navigation and Object Retrieval in Assistive Robot Dogs

Qian Yin·Ruiping Liu·Kunyu Peng·Jianxiang Man·Isik Baran Sandan·Junwei Zheng·Yufan Chen·Di Wen·Kailun Yang·Rainer Stiefelhagen
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Abstract

Consider one robot guide dog escorting a blind user to a seat while a second retrieves and delivers an object. We introduce \textsc{GuideFetch}, a framework for coordinating this concurrent guide-and-fetch mission with heterogeneous robots. A large language model (LLM) instantiates a schedule-conditioned four-action schema; deterministic normalization and validation enforce registered targets, robot capabilities, and the selected schedule, while robot and object states govern execution and completion. We record 360 simulator runs over 90 scene--seed combinations under scripted and online plan-provenance conditions. All 180 online responses validate on the first request and match their scripted references, so the plan-provenance comparison tests normalized-plan agreement rather than a distinct execution factor. A simulator-free mutation test accepts two valid controls and rejects all 32 rule-violating variants. Across 90 scene--seed cases per schedule, sequential and parallel execution achieve $72/90$ and $71/90$ operational successes. Among 56 common successes, the implemented role-reassigned parallel protocol reduces mean makespan by 41.3\%. This system-level gain combines role assignment, action overlap, and scene geometry; state checks distinguish plan validity from verified mission completion.

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Qian Yin, Ruiping Liu, Kunyu Peng, Jianxiang Man, Isik Baran Sandan, Junwei Zheng, Yufan Chen, Di Wen, Kailun Yang, Rainer Stiefelhagen. 2026-08-18. GuideFetch: A Task Coordination Framework for Concurrent Navigation and Object Retrieval in Assistive Robot Dogs. https://arxiv.org/abs/2608.18292

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