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This paper introduces GuideFetch, a coordination framework that enables concurrent navigation and object retrieval by a team of assistive robot dogs, addressing the challenge of overlapping tasks in real-world scenarios. By utilizing a schedule-conditioned action schema generated from natural language instructions, the framework ensures that proposed actions are validated against robot capabilities and target availability before execution. The results from a matched study demonstrate that both sequential and parallel execution strategies achieve high operational success rates, with parallel execution significantly reducing task completion time by 41.3%.
Parallel execution of tasks in assistive robots can cut mission completion time by over 40%, revolutionizing how we think about robot coordination in dynamic environments.
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.
