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

合作研究：HCC：小型：利用包裹式触觉显示器来传达机器人学习并加速人类教学

• 批准号: 2129155
• 负责人: Laura Blumenschein
• 金额: $ 24.95万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2129155&HistoricalAwards=false
• 关键词: Collaborative; Research; HCC; Small; Leveraging

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.

人类通过演示和身体纠正（想想教练通过所需的运动来指导运动员）擅长教授身体活动和任务），人类学习者经常使用言语和非语言信号来传达他们的理解或混乱。人类和机器人之间也使用了类似的教学方法，使人类自然地展示了诸如烹饪或家具组件之类的任务，并在机器人的运动中纠正了错误。尽管机器人在理解人类教师的示范和纠正方面取得了重大进步，但他们缺乏一种有效的方式来传达自己的工作和不了解的事情，因此人类老师可能不知道何时或是否准备好自行执行任务。该项目将通过为机器人武器在学习时与人类教师沟通的新方法来解决这一沟通差距。调查人员将将狂热的皮肤显示（可控气泡的阵列）附加到机器人手臂上，并产生触摸感，以将机器人的理解或混乱传达给人类老师。从机器人学习的过程中，更好，更容易理解的沟通将使用户能够更有效地训练或再培训机器人，并更好地了解何时准备部署，而无需有关机器人如何运作的专业知识。这些功能将使机器人臂更具吸引力的中小型制造商工具，从而使它们可以灵活地自动化一些制造任务。关于传达机器人臂学习状态的经验教训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加全面，并为捕获和纠正错误的机会。该项目的目的是表征人类对触觉的表征如何围绕机器人武器展示机器人，并通过机器人的方式捕获这些机器人的方式，并通过这些方式捕获这些反馈和交流，并进行了交流的回报，并将其交流。先前的工作使机器人能够从物理演示中学习；但是，使这项学习与人类老师透明同样重要。该项目将从算法和哈蒂奇的角度来促进透明且可解释的机器人学习。研究人员的团队将i）表征人类感知的探索性触觉反馈的类型和模式，ii）将机器人的复杂且高维的奖励学习嵌入到低维触觉反馈中，以及iii）模型），人类如何解释机器人的反馈。这些步骤最终将使人类教师对机器人的理解有所了解，从而改善他们的示威。每个贡献将在人类主题研究中使用商业机器人组进行评估。该项目有潜力通过使机器人武器直观，透明和用户友好的过程来推进中小型制造业的机器人。该奖项反映了NSF的法定任务，并通过使用基金会的智力优点和更广泛的影响标准来评估NSF的法定任务。

项目成果

Wrapped Haptic Display for Communicating Physical Robot Learning

用于交流物理机器人学习的包裹式触觉显示器

• DOI: 10.1109/robosoft54090.2022.9762210
• 发表时间: 2022
• 期刊: 2022 IEEE 5th International Conference on Soft Robotics (RoboSoft
• 作者: Valdivia, Antonio Alvarez;Shailly, Ritish;Seth, Naman;Fuentes, Francesco;Losey, Dylan P.;Blumenschein, Laura H.
• 通讯作者: Blumenschein, Laura H.

Wrapping Haptic Displays Around Robot Arms to Communicate Learning

将触觉显示器包裹在机器人手臂周围以传达学习信息

• DOI: 10.1109/toh.2023.3240400
• 发表时间: 2023
• 期刊: IEEE Transactions on Haptics
• 影响因子: 2.9
• 作者: Alvarez Valdivia, Antonio;Habibian, Soheil;Mendenhall, Carly A.;Fuentes, Francesco;Shailly, Ritish;Losey, Dylan P.;Blumenschein, Laura H.
• 通讯作者: Blumenschein, Laura H.

Perception of and Response to a Haptic Device as a Function of Signal Complexity

作为信号复杂性函数的触觉设备的感知和响应

• 发表时间: 2023
• 期刊: IEEE World Haptics Conference
• 作者: Alvarez Valdivia, Antonio;Blumenschein, Laura H.
• 通讯作者: Blumenschein, Laura H.