论文/arXiv 2608.18292·2 个月前

GuideFetch：用于辅助机器狗并发导航与物体取回的任务协调框架

GuideFetch用LLM生成受日程约束的动作方案，协调异构机器狗导盲与取物，并在仿真中验证。

AI 整理

• 本体 SDK 与控制
• 感知与导航
• 具身模型

原文链接：GuideFetch: A Task Coordination Framework for Concurrent Navigation and Object Retrieval in Assistive Robot Dogs（arXiv 2608.18292 · 发布日期：2026-08-18）

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

GuideFetch用LLM生成受日程约束的动作方案，协调异构机器狗导盲与取物，并在仿真中验证。

核心要点

• 面向一只导盲护送用户就座、另一只取送物体的并发协作任务。
• LLM实例化四动作模式，归一化与校验约束目标、能力和日程。
• 360次仿真中，在线计划均首请求通过验证并匹配脚本参考。
• 顺序与并行分别成功72/90、71/90；共同成功中并行平均耗时降41.3%。

验证范围与局限：验证范围为90个场景-种子组合的仿真运行，以及不含仿真的突变测试。

英文摘要 (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.

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

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2608.18292：GuideFetch: A Task Coordination Framework for Concurrent Navigation and Object Retrieval in Assistive Robot Dogs访问 2026-10-21