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通过被动增强现实与主动触觉反馈传达推断出的目标

Communicating Inferred Goals with Passive Augmented Reality and Active Haptic Feedback

Virginia Tech（弗吉尼亚理工大学）

James F. Mullen, Josh Mosier, Sounak Chakrabarti, Anqi Chen, Tyler White, Dylan P. Losey

备注：
8 pages, 5 figures
发表信息：
IEEE Robotics and Automation Letters 6.4 (2021) 8522-8529
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AI总结：

针对人类遥操作机器人时难知晓机器人推断结果的问题，提出结合被动AR可视化与主动触觉腕带提示的多模态反馈框架，可提升人类教机器人的效率并减少交互时长。

摘要：

机器人在与人类交互的过程中进行学习。设想人类遥操作一台辅助机械臂的场景：在人类引导并纠正机械臂运动的过程中，机器人会收集与人类期望任务相关的信息。但人类如何知晓机器人已经推断出了什么呢？目前的方法通常聚焦于传达意图：例如通过清晰易懂的动作或手势来表明机器人的规划。然而，要形成机器人推断的闭环，仅仅展示机器人的当前策略还不够：机器人还应当展示它认为可能性较高的备选方案，并在需要额外指导时提示人类教师。\n本文提出了一种用于传达机器人推断结果的多模态方法，该方法结合了被动反馈与主动反馈。具体而言，我们利用信息丰富的增强现实（AR）来被动可视化机器人的推断结果，并使用能吸引注意力的触觉腕带来主动提示并引导人类的教学过程。我们将该系统应用于共享自主任务，这类任务中机器人必须实时推断人类的目标。在此场景下，我们将被动模态与主动模态整合到一个统一的算法框架中，该框架可确定何时提供反馈以及提供何种类型的反馈。\n实验表明，被动与主动反馈相结合的效果优于单模态基线方法；在一项线下用户研究中，我们证实这种集成方法提升了人类教授机器人的效率，同时减少了人类与机器人交互的时长。相关视频见：https://youtu.be/swq_u4iIP-g

英文摘要：

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

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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