让机器人用自然语言预告动作，提升人机协作透明度。
CoRI: Communication of Robot Intent for Physical Human-Robot Interaction
- 从运动轨迹和视觉感知自动生成意图描述
- 用户研究显示沟通清晰度显著提升
- 适合需高透明度的医疗辅助机器人场景
清晰传达机器人意图能增强物理人机交互（pHRI）中的透明性与可解释性，尤其在涉及直接人机接触的辅助任务中。我们提出CoRI，一个自动从机器人运动规划与视觉感知生成自然语言意图说明的流水线。该方法首先处理机器人视角图像，识别人体姿态与关键环境特征；接着将规划的3D空间轨迹（含速度与力）编码至该视图，实现路径与动态的视觉锚定；最后通过视觉-语言模型结合视觉表示，理解计划动作的上下文并生成简洁、面向用户的语句，无需依赖任务特定信息。在两个不同机器人上进行的机器人辅助进餐、洗浴和剃须任务的用户研究显示，与基线通信策略相比，CoRI在沟通清晰度上具有统计学显著差异。CoRI不仅有效传达机器人的高层意图，还准确传递运动细节及用户需配合的动作。项目视频与代码见：https://cori-phri.github.io/
原文摘要 · Abstract (English)
Clear communication of robot intent fosters transparency and interpretability in physical human-robot interaction (pHRI), particularly during assistive tasks involving direct human-robot contact. We introduce CoRI, a pipeline that automatically generates natural language communication of a robot's upcoming actions directly from its motion plan and visual perception. Our pipeline first processes the robot's image view to identify human poses and key environmental features. It then encodes the planned 3D spatial trajectory (including velocity and force) onto this view, visually grounding the path and its dynamics. CoRI queries a vision-language model with this visual representation to interpret the planned action within the visual context before generating concise, user-directed statements, without relying on task-specific information. Results from a user study involving robot-assisted feeding, bathing, and shaving tasks across two different robots indicate that CoRI leads to statistically significant difference in communication clarity compared to a baseline communication strategy. Specifically, CoRI effectively conveys not only the robot's high-level intentions but also crucial details about its motion and any collaborative user action needed. Video and code of our project can be found on our project website: https://cori-phri.github.io/
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