外观
论文arXiv 2610.0747723 天前
MRPilot：通过混合现实监督和干预基于 LLM 的多机器人团队
原文链接：MRPilot: Supervising and Intervening LLM-Based Multi-Robot Teams through Mixed Reality（arXiv 2610.07477 · 发布日期：2026-10-05）
MRPilot: Supervising and Intervening LLM-Based Multi-Robot Teams through Mixed Reality
为解决基于LLM的多机器人系统难以检查和修改，MRPilot用混合现实监督干预，并在VR模拟家庭中验证。
核心要点
- LLM可用自然语言指挥异构多机器人，但任务解释、分配与协调难检查和修改。
- MRPilot基于12名非专家形成性研究，围绕四个监督与干预阶段设计。
- 系统在同步情境视图和概览视图中，以结构化承诺呈现计划与执行状态。
- 20人被试内研究显示，相比传统对话界面，MRPilot降低负荷并提升态势感知、透明度、信任和控制感。
验证范围与局限：验证范围为20名参与者在虚拟现实模拟家庭中的被试内研究。
英文摘要 (Abstract)
Large language models (LLMs) let users direct heterogeneous multi-robot systems (MRS) through natural language, but make task interpretation, robot assignment, and coordination difficult to inspect and change. Based on a formative study with 12 non-expert users, we developed MRPilot, a mixed reality system organized around four stages of supervision and intervention. MRPilot represents robot-team plans and execution states as structured commitments shared across synchronized situated and overview views. Across four stages, it helps users resolve ambiguous references (Forming), review plans before execution (Reviewing), monitor distributed execution (Following), and make robot-level or team-level changes when problems arise (Repairing). In a within-subjects study with 20 participants in a virtual reality-simulated home, MRPilot reduced workload, increased situational awareness, transparency, trust, and perceived control compared with a conventional LLM-based conversational interface using the same LLM planner and robot capabilities. We provide design implications for multi-scale intervention, adaptive supervision, and calibrated reliance in LLM-based MRS.
作者：Xiaoran Yang, Xun Qian, Yang Zhan, Nathan Tran, Ziyi Liu, Qiao Jin
来源
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