Robots learn as they interact with humans. Consider a human teleoperating an assistive robot arm: as the human guides and corrects the arm's motion, the robot gathers information about the human's desired task. But how does the human know what their robot has inferred? Today's approaches often focus on conveying intent: for instance, upon legible motions or gestures to indicate what the robot is planning. However, closing the loop on robot inference requires more than just revealing the robot's current policy: the robot should also display the alternatives it thinks are likely, and prompt the human teacher when additional guidance is necessary. In this paper we propose a multimodal approach for communicating robot inference that combines both passive and active feedback. Specifically, we leverage information-rich augmented reality to passively visualize what the robot has inferred, and attention-grabbing haptic wristbands to actively prompt and direct the human's teaching. We apply our system to shared autonomy tasks where the robot must infer the human's goal in real-time. Within this context, we integrate passive and active modalities into a single algorithmic framework that determines when and which type of feedback to provide. Combining both passive and active feedback experimentally outperforms single modality baselines; during an in-person user study, we demonstrate that our integrated approach increases how efficiently humans teach the robot while simultaneously decreasing the amount of time humans spend interacting with the robot. Videos here: https://youtu.be/swq_u4iIP-g
翻译:机器人在与人类互动时会学习。 考虑一个人类远程操作的辅助机器人手臂: 当人类向导和纠正手臂运动时, 机器人会收集人类想要的任务的信息。 但是人类如何知道机器人的推断? 今天的方法往往侧重于传递意图: 例如, 以清晰的动作或手势来显示机器人正在计划什么。 但是, 关闭机器人环绕不仅需要披露机器人的现行政策: 机器人也应该显示它认为可能的选择, 并在需要更多指导时促使人类老师。 在本文中, 我们提出一种多式方法来传递机器人的推断, 将被动和主动反馈结合起来。 具体地说, 我们利用信息丰富的事实来被动地直观显示机器人所推断的内容, 以及吸引注意的手腕带来积极提示和引导人类的教学。 我们运用我们的系统来共享自主任务, 机器人必须在这里显示它所认为的替代目标, 并在需要更多指导的时候, 我们把被动和主动的方式融入一个单一的算法框架, 将被动和主动的推论的推论结合被动的推论框架, 和主动式的推论式的推论式的推论在实验性模型研究期间, 我们如何同时和不断地模拟地模拟的推算出一个模型的模型的模型的推算出一个模型的模型, 。