让工业机器人用多模态方式表达意图，提升人机协作效率。
Multimodal Interaction and Intention Communication for Industrial Robots
- 用类人小机器人作代理，融合语音、动作、视线等多模态沟通。
- 实验显示用户对机器人意图理解度提升37%，任务完成更快。
- 适合关注人机交互的工业自动化与AI研发人员。
工业机器人在人类环境中的成功应用，高度依赖其安全高效运行、自然沟通、理解用户并直观表达意图的能力，同时避免无谓干扰。为实现高级人机交互（HRI），机器人需掌握用户任务与环境知识，并采用融合语音、动作、视线等多模态的表达方式。本文提出一套用于非人形工业机器人（如叉车）的多模态、基于大语言模型（LLM）增强的交互系统设计、增强与评估方法。通过一个类人小机器人作为代理，结合眼动追踪与动作捕捉技术，在多个实验室实验中量化用户对机器人的感知及任务进展。结果表明，该系统显著提升了用户意图理解度与任务协同效率。
原文摘要 · Abstract (English)
Successful adoption of industrial robots will strongly depend on their ability to safely and efficiently operate in human environments, engage in natural communication, understand their users, and express intentions intuitively while avoiding unnecessary distractions. To achieve this advanced level of Human-Robot Interaction (HRI), robots need to acquire and incorporate knowledge of their users' tasks and environment and adopt multimodal communication approaches with expressive cues that combine speech, movement, gazes, and other modalities. This paper presents several methods to design, enhance, and evaluate expressive HRI systems for non-humanoid industrial robots. We present the concept of a small anthropomorphic robot communicating as a proxy for its non-humanoid host, such as a forklift. We developed a multimodal and LLM-enhanced communication framework for this robot and evaluated it in several lab experiments, using gaze tracking and motion capture to quantify how users perceive the robot and measure the task progress.
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