用户可用自然语言实时调整机器人动作，系统还能回应说明意图。
Bidirectional Human-Robot Communication for Physical Human-Robot Interaction
- 用大模型理解用户指令，动态修改机器人的位置、速度和力
- 18位老年人参与测试，双向语音反馈显著提升交互体验
- 适合需要直观人机协作的辅助机器人场景
有效的物理人机交互需要系统既能适应用户偏好，又能透明表达自身行为。本文提出BRIDGE系统，实现物理辅助中的双向人机通信。该方法允许用户通过自然语言实时修改机器人的规划轨迹（位置、速度、力），利用大语言模型（LLM）结合运动计划与对话历史，理解指令隐含的轨迹调整。重要的是，系统会针对用户输入提供口头反馈，确认变更或提出澄清问题。我们在18位老年人中开展三类辅助任务的用户研究，对比了含语音反馈的BRIDGE、无反馈消融版本及基线。结果表明，参与者能成功实现实时轨迹修改；双向反馈显著提升了交互性和透明度评分，证明机器人语音响应对提升使用直觉性至关重要。视频与代码见项目网站：https://bidir-comm.github.io/
原文摘要 · Abstract (English)
Effective physical human-robot interaction requires systems that are not only adaptable to user preferences but also transparent about their actions. This paper introduces BRIDGE, a system for bidirectional human-robot communication in physical assistance. Our method allows users to modify a robot's planned trajectory -- position, velocity, and force -- in real time using natural language. We utilize a large language model (LLM) to interpret any trajectory modifications implied by user commands in the context of the planned motion and conversation history. Importantly, our system provides verbal feedback in response to the user, either assuring any resulting changes or posing a clarifying question. We evaluated our method in a user study with 18 older adults across three assistive tasks, comparing BRIDGE to an ablation without verbal feedback and a baseline. Results show that participants successfully used the system to modify trajectories in real time. Moreover, the bidirectional feedback led to significantly higher ratings of interactivity and transparency, demonstrating that the robot's verbal response is critical for a more intuitive user experience. Videos and code can be found on our project website: https://bidir-comm.github.io/
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