Collaborative Research: HCC: Small: Leveraging a Wrapped Haptic Display to Communicate Robot Learning and Accelerate Human Teaching

人类擅长通过示范和身体矫正来教授身体活动和任务(想想教练引导运动员完成想要的动作)，而人类学习者经常使用语言和非语言信号来交流他们的理解或困惑。人类和机器人之间也使用了类似的教学方法，使人类能够自然地演示烹饪或家具组装等任务，并纠正机器人动作中的错误。虽然机器人在理解人类老师的示范和纠正方面取得了很大进步，但它们缺乏一种有效的方式来沟通它们所做的和不理解的事情，因此人类教师可能不知道机器人何时或是否准备好独自执行任务。该项目将通过开发机器人手臂在人类教师学习时与他们交流的新方法来解决这一沟通差距。研究人员将在机械臂上安装触觉皮肤显示器(可控气泡阵列)，并创造触摸感觉，将机器人的理解或困惑传达给人类老师。机器人在学习过程中更好、更容易理解的沟通将使用户能够更有效地培训或再培训机器人，并更好地知道他们何时准备部署，而不需要关于机器人如何工作的专业知识。这些功能将使机械臂对中小型制造商来说更具吸引力的工具，使他们能够灵活地自动化一些制造任务。关于传达机械臂学习状态的经验也可以应用于其他计算机和机器人系统，使机器人和计算机学习的不透明过程更容易理解，并提供机会捕捉和纠正错误。本项目的目标是描述人类如何感知机械臂周围的触觉皮肤显示，并正式确定机器人如何通过这些触觉阵列捕获和交流反馈。先前的工作使机器人能够从物理演示中学习；然而，同样重要的是使这种学习对人类教师透明。这个项目将从算法和触觉的角度推进透明和可解释的机器人学习。研究团队将i)表征人类感知的探索性触觉反馈的类型和模式，ii)将机器人复杂而高维的奖励学习嵌入到低维触觉反馈中，iii)建立人类如何解释机器人反馈的模型。这些步骤最终将使人类教师了解机器人的理解，从而改进他们的演示。每一项贡献都将在具有商业机械臂的人体研究中进行评估。该项目通过使教授机器人手臂的过程直观、透明和用户友好，有可能推动中小型制造业中的机器人技术。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Humans excel at teaching physical activities and tasks through demonstration and physical correction (think of a coach guiding an athlete through a desired motion), and human learners often use verbal and nonverbal signals to communicate their understanding or confusion. Similar approaches to teaching have been used between humans and robots, allowing humans to naturally demonstrate tasks, like cooking or furniture assembly, and correct errors in the motions of the robots. While robots have made great advances in understanding the demonstrations and corrections from human teachers, they have lacked an effective way to communicate what they do and do not understand, so human teachers may not know when or if a robot is ready to carry out a task by itself. This project will address this communication gap by developing new ways for robot arms to communicate to human teachers as they learn. The investigators will attach haptic skin displays (arrays of controllable bubbles) to a robotic arm and create touch sensations to communicate the robot’s understanding or confusion to the human teacher. Better and more understandable communication from robots as they learn will allow users to train or retrain robots more efficiently, and to better know when they are ready to deploy, without needing specialized knowledge about how robots function. These features will make robot arms more attractive tools for small and mid-sized manufacturers, allowing them to flexibly automate some manufacturing tasks. The lessons learned about communicating the learning state of robotic arms can also be applied to other computer and robotic systems, making the opaque process of robot and computer learning more comprehensible and giving the opportunity to catch and correct errors.The goal of this project is to characterize how humans perceive haptic skin displays wrapped around robot arms, and to formalize how robots capture and communicate feedback through these haptic arrays. Prior work enables robots to learn from physical demonstrations; however, it is equally important to make this learning transparent to the human teacher. This project will advance transparent and interpretable robot learning from an algorithmic and haptic perspective. The team of investigators will i) characterize the types and patterns of exploratory haptic feedback the human perceives, ii) embed the robot's complex and high-dimensional reward learning into low-dimensional haptic feedback, and iii) model how humans interpret the robot's feedback. These steps will ultimately provide human teachers with an awareness of the robot’s understanding and thereby improve their demonstrations. Each contribution will be evaluated in human subject studies with a commercial robot arm. This project has the potential to advance robotics in small and mid-sized manufacturing by making the process of teaching robot arms intuitive, transparent, and user-friendly.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.