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/

翻译：有效的物理人机交互不仅需要系统能够适应用户偏好，还需要其行为具有透明度。本文介绍了BRIDGE，一个用于物理辅助任务的双向人机通信系统。我们的方法允许用户使用自然语言实时修改机器人的规划轨迹——包括位置、速度和力。我们利用大语言模型（LLM），在规划运动与对话历史的上下文中解析用户指令所隐含的任何轨迹修改。重要的是，我们的系统会向用户提供语言反馈，或确认修改结果，或提出澄清性问题。我们在一项有18名老年人参与的用户研究中评估了我们的方法，研究涵盖了三个辅助任务，并将BRIDGE与一个无语言反馈的消融版本以及一个基线系统进行了比较。结果表明，参与者能够成功使用该系统实时修改轨迹。此外，双向反馈使得交互性和透明度的评分显著更高，这证明了机器人的语言反馈对于获得更直观的用户体验至关重要。视频和代码可在我们的项目网站上找到：https://bidir-comm.github.io/