探索大型语言模型在人机协作中的整合:对表现,心理压力和信任的影响
Bingyi Su1, Fangyuan Cheng1, Lu Lu1
1Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, United States of America.
Applied ergonomics
|January 21, 2026
概括
大型语言模型 (LLM) 通过提高任务效率和减少用户压力来增强人机协作 (HRC). 此外,LLM集成也提高了用户对机器人系统的信任,以完成协作任务.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人与计算机的交互
- 人工智能的人工智能
背景情况:
- 大型语言模型 (LLM) 越来越多地集成到机器人系统中.
- 了解LLM增强机器人对人机协作 (HRC) 的影响对于有效的交互设计至关重要.
- 课程课程提供了更自然的沟通和减少HRC认知负载的潜力.
研究的目的:
- 调查LLM增强的机器人系统对用户性能,心理压力和在HRC任务中的信任的影响.
- 为了比较具有和没有LLM支持的HRC场景.
- 评估用户体验的主观和客观指标.
主要方法:
- 使用了两个HRC场景 (物体交付,遵循指令).
- 参与者完成任务与或没有LLM支持.
- 性能指标包括任务完成时间和口头命令.
- 使用NASA-TLX和皮反应 (GSR) 来测量心理压力.
- 通过SHAPE信任指数和眼睛追踪来评估信任.
主要成果:
- 整合LLM显著提高了任务效率 (减少完成时间,减少命令).
- 主观心理压力,特别是心理需求,努力和丧,减少了.
- 参与者在多个维度 (实用性,可靠性,准确性,易用性) 中对LLM支持的系统表示更高的信任.
- GSR数据表明参与度增加或积极的情绪激活,而眼睛跟踪显示没有显著差异.
结论:
- 课程课程显示出通过促进自然沟通和减少心理工作量来增强HRC的巨大潜力.
- 增加用户信任是LLM集成到协作机器人的关键好处.
- 需要对系统透明度进行进一步的研究,以确保用户对LLM-powered HRC的持续信任.
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