面向普适机器人中用户介导的自修复：基于目标导向的智能体AI
Toward User-Mediated Self-Repair in Ubiquitous Robots Through Goal-Oriented Agentic AI
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中文总结 AI 辅助
本文提出一种目标导向的智能体AI架构，通过情境化对话使非专家用户完成普适机器人技术修复，实验显示95%任务完成率，并验证了其在物理环境中的稳健性。
中文摘要 AI 辅助
普适机器人系统往往缺乏传统的可视化界面，因此需要具有韧性的自然语言交互来支持维护和修复任务。本文提出了一种目标导向的智能体AI架构，旨在通过情境化对话使非专家用户能够执行技术修复。该框架采用多层方法，将高层战略规划与反应式对话执行解耦，从而将不受约束的人类指令转化为结构化的目标层级。我们开展了一项涉及二十名参与者的研究，使用物理硬件测试平台评估系统效能。该架构实现了95%的任务完成率，参与者在适应对话偏离和语言变异的实时引导下报告了积极的自我效能感。与在线基线的比较分析显示，转向物理环境显著降低了感知社会存在感（p=.0005）以及信任和能力（p=.037），而智能体框架在整个交互过程中保持稳健。这些发现表明，目标导向的智能体AI能够通过赋能用户在普适环境中执行关键维护，支持穿戴式技术的可持续性。
英文摘要
Ubiquitous robotic systems often lack traditional visual interfaces, necessitating resilient natural language interaction for maintenance and repair tasks. This paper presents a goal oriented agentic AI architecture designed to enable non-expert users to perform technical repairs through situated dialogue. The framework utilizes a multi-layered approach that decouples high-level strategic planning from reactive conversational execution to transform unconstrained human instructions into a structured hierarchy of goals. We conducted a study involving twenty participants to evaluate the system's efficacy using a physical hardware testbed. The architecture achieved a 95\% task completion rate, and participants reported positive self-efficacy following real-time guidance that adapted to conversational diversions and linguistic variations. A comparative analysis with an online baseline revealed that the transition to a physical environment significantly decreased perceived social presence (p=.0005), and trust and competence, (p=.037), while the agentic framework remained robust throughout the interaction. These findings indicate that goal oriented agentic AI can support the sustainability of body-worn technologies by empowering users to perform critical maintenance in ubiquitous contexts.
发表机构
- IT-University of Copenhagen（哥本哈根信息技术大学）
机构由 AI 辅助整理，请以论文原文为准。
