GuideFetch：用于辅助机器狗并发导航与物体检索的任务协调框架
GuideFetch: A Task Coordination Framework for Concurrent Navigation and Object Retrieval in Assistive Robot Dogs
浏览论文内容
中文总结 AI 辅助
针对异构辅助机器狗的导航与物体检索并发任务，提出GuideFetch协调框架，通过LLM规划与状态验证提升执行效率，并行执行可缩短41.3%平均总完成时间。
中文摘要 AI 辅助
考虑这样一种场景：一台机器人导盲犬护送一名视障用户前往一个空座位，同时另一台辅助机器狗并发检索一杯咖啡并将其送到同一座位。该场景因导航与物体检索可重叠执行而催生了并发执行需求。句法上有效的大语言模型（LLM）规划仍可能违反实体约束，而看似成功的控制器运动本身并不能证明任务完成。我们提出\textsc{GuideFetch}，这是一个用于异构引导者与检索者团队并发导航和物体检索的协调框架。LLM 根据自然语言指令实例化一个基于调度的四动作模式。执行前，对机器人、技能和目标别名进行归一化，并针对注册目标、机器人能力和所选调度验证拟议动作。随后，机器人与物体状态控制顺序和并行执行。在跨90种场景与随机种子组合的匹配2×2研究中（共360次执行），全部180次在线LLM响应均无需 fallback或重放即可通过验证，且与相应的脚本计划匹配。对于每个规划器源，顺序执行与并行执行分别取得72/90和71/90的操作成功率。在两个调度均完成的56个案例中，并行执行将平均总完成时间缩短了41.3%。在该受控场景中，角色专业化与动作重叠缩短了完成任务的时间，而状态检查区分了计划有效性与经验证的任务完成情况。源代码将公开提供。
英文摘要
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.
发表机构
- Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
- Hunan University（湖南大学）
机构由 AI 辅助整理，请以论文原文为准。
