GuideFetch: 支援ロボット犬における並行ナビゲーションと物体回収のためのタスク調整フレームワーク
GuideFetch: A Task Coordination Framework for Concurrent Navigation and Object Retrieval in Assistive Robot Dogs
盲人ユーザーを案内するロボット犬と、コーヒーを回収して届ける別のロボット犬が並行して動作する状況を想定し、LLMを用いて計画を検証・実行する調整フレームワークを提案した。
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著者: Qian Yin, Ruiping Liu, Kunyu Peng, Jianxiang Man, Isik Baran Sandan, Junwei Zheng, Yufan Chen, Di Wen, Kailun Yang, Rainer Stiefelhagen
分類: cs.RO, cs.CV
原文アブストラクト
Consider a robot guide dog escorting a blind user to an available seat while a second assistive robot dog concurrently retrieves a cup of coffee and delivers it to the same seat. This setting motivates concurrent execution because navigation and object retrieval can overlap. A syntactically valid Large Language Model (LLM) plan may still violate embodiment constraints, and successful-looking controller motion does not by itself establish task completion. We introduce \textsc{GuideFetch}, a coordination framework for concurrent navigation and object retrieval by a heterogeneous guider and fetcher team. An LLM instantiates a schedule-conditioned four-action schema from a natural-language instruction. Before execution, robot, skill, and target aliases are normalized, and proposed actions are validated against registered targets, robot capabilities, and the selected schedule. Robot and object states then govern sequential and parallel execution. In a matched $2\times2$ study across 90 combinations of scene and seed (360 executions), all 180 online LLM responses validate without fallback or replay and match the corresponding scripted plans. For each planner source, sequential and parallel execution achieve $72/90$ and $71/90$ operational successes, respectively. Among the 56 cases completed by both schedules, parallel execution reduces mean makespan by 41.3\%. Within this controlled setting, role specialization and action overlap shorten completed missions, while state checks distinguish plan validity from verified mission completion. Source code will be available.