提出多模态意图通信框架，提升人机协作中的透明度与信任。
Designing Intent: A Multimodal Framework for Human-Robot Cooperation in Industrial Workspaces
- 基于情境意识框架，构建意图内容、规划时长与模态的三维设计空间。
- 通过视觉、听觉、触觉多通道传递意图，适配动态协作场景。
- 适合工业人机协作系统设计者参考，推动可信机器人交互发展。
随着机器人进入协同工作环境，确保人类工人与机器人系统之间的相互理解成为建立信任、保障安全与提升效率的前提。本文以AIMotive项目中人与协作机器人共同完成装配任务的合作场景为基础，主张采用结构化方法实现意图沟通。结合基于情境意识的代理透明性（SAT）框架与任务抽象层级概念，提出一个包含意图内容（SAT1、SAT3）、规划时长（从操作到战略）及模态（视觉、听觉、触觉）的多维设计空间。该空间可指导针对动态协作环境定制的多模态通信策略设计。本文旨在为未来支持工作场所透明人机交互的设计工具包奠定概念基础，同时指出关键开放问题与设计挑战，并提出面向多模态、自适应与可信协作的共享研究议程。
原文摘要 · Abstract (English)
As robots enter collaborative workspaces, ensuring mutual understanding between human workers and robotic systems becomes a prerequisite for trust, safety, and efficiency. In this position paper, we draw on the cooperation scenario of the AIMotive project in which a human and a cobot jointly perform assembly tasks to argue for a structured approach to intent communication. Building on the Situation Awareness-based Agent Transparency (SAT) framework and the notion of task abstraction levels, we propose a multidimensional design space that maps intent content (SAT1, SAT3), planning horizon (operational to strategic), and modality (visual, auditory, haptic). We illustrate how this space can guide the design of multimodal communication strategies tailored to dynamic collaborative work contexts. With this paper, we lay the conceptual foundation for a future design toolkit aimed at supporting transparent human-robot interaction in the workplace. We highlight key open questions and design challenges, and propose a shared agenda for multimodal, adaptive, and trustworthy robotic collaboration in hybrid work environments.
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