Assistive robot arms can help humans by partially automating their desired tasks. Consider an adult with motor impairments controlling an assistive robot arm to eat dinner. The robot can reduce the number of human inputs -- and how precise those inputs need to be -- by recognizing what the human wants (e.g., a fork) and assisting for that task (e.g., moving towards the fork). Prior research has largely focused on learning the human's task and providing meaningful assistance. But as the robot learns and assists, we also need to ensure that the human understands the robot's intent (e.g., does the human know the robot is reaching for a fork?). In this paper, we study the effects of communicating learned assistance from the robot back to the human operator. We do not focus on the specific interfaces used for communication. Instead, we develop experimental and theoretical models of a) how communication changes the way humans interact with assistive robot arms, and b) how robots can harness these changes to better align with the human's intent. We first conduct online and in-person user studies where participants operate robots that provide partial assistance, and we measure how the human's inputs change with and without communication. With communication, we find that humans are more likely to intervene when the robot incorrectly predicts their intent, and more likely to release control when the robot correctly understands their task. We then use these findings to modify an established robot learning algorithm so that the robot can correctly interpret the human's inputs when communication is present. Our results from a second in-person user study suggest that this combination of communication and learning outperforms assistive systems that isolate either learning or communication.

翻译：辅助机器人手臂能够通过部分自动化执行人类期望的任务来提供帮助。考虑一位运动障碍的成年人操作辅助机器人手臂用餐的场景：机器人通过识别人类意图（例如，拿取叉子）并辅助完成该任务（例如，向叉子方向移动），能够减少人类输入的次数及其精度要求。现有研究主要聚焦于学习人类任务并提供有意义的辅助。然而，在机器人学习和辅助的过程中，我们还需确保人类理解机器人的意图（例如，人类是否知道机器人正在伸向叉子？）。本文研究了将机器人学到的辅助信息反馈给操作者的沟通效果。我们不聚焦于具体的沟通接口，而是通过实验与理论模型探讨：a) 沟通如何改变人类与辅助机器人手臂的交互方式，以及b) 机器人如何利用这些变化更好地对齐人类意图。我们首先开展了在线与线下用户研究，让参与者操作提供部分辅助的机器人，并测量在有/无沟通条件下人类输入的变化。结果表明，当机器人错误预测人类意图时，有沟通情况下人类更倾向于干预；当机器人正确理解任务时，人类更倾向于释放控制权。基于这些发现，我们改进了现有机器人学习算法，使机器人在有沟通时能正确解读人类输入。第二次线下用户研究的结果表明，沟通与学习的结合优于仅依赖学习或仅依赖沟通的辅助系统。