NRI: INT: Co-Robot Controllers for Human-Like Physical Interaction and Improved Motor Learning

NRI：INT：用于类人物理交互和改进运动学习的协作机器人控制器

• 批准号: 2024488
• 负责人: Jose Pons
• 金额: $ 149.9万
• 依托单位: Rehabilitation Institute of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024488&HistoricalAwards=false
• 关键词: NRI; INT; Co; Robot; Controllers

Human-like robots, which perform shared tasks with humans in common workspaces, have increased application in home and office services, in rehabilitation and skill training, and in capability enhancement. Digital technologies have enabled these ubiquitous robots with easier and faster human-like audio-visual interactions. However, the lack of physical interaction has posed a significant barrier to human-robot shared tasks, which is essential in applications that require complex physical interactions during the shared tasks. A great number of studies on physical interaction between human dyads or pairs have been conducted during the past decade. Several studies showed that dyadic physical interaction improves the performance of two individuals working together on shared tasks compared to working alone. Amazingly, results showed that human dyads not only perform better, but also learn new tasks faster. Therefore, understanding how human dyads physically interact can be applied while provide insight in developing human-like robots, which to mimic human behavior when performing shared tasks with other humans or robots. The vision of this work is to understand the underlying mechanisms of human dyadic physical interaction that lead to improvement in motor performances and learning rates, and to integrate this knowledge in developing new robotic controllers. Additionally, the infrastructure of the controller and findings will be shared as open source, and educational programs will be developed to support the advancement of the field. There are many factors that define the physical interaction between dyads, such as the interactive behavior (i.e., collaboration, competition, cooperation), haptic connection (i.e., impedance levels), and skill levels of the dyads (i.e., novice-novice and expert-novice). Therefore, a systematic approach that can quantitatively compare each condition is crucial. To realize the vision of this work, a novel exoskeleton-based dyadic interaction infrastructure will be implemented to study physical dyadic interaction with multiple DOF and multiple contact points by providing virtual connections of varying and controllable impedance between the exoskeleton systems. This infrastructure will be utilized to reveal comprehensive knowledge of how the task performance and motor learning of peers in dyadic haptic interaction are affected by 1) physical interactive behaviors, 2) impedance of the multi-joint virtual connection, and 3) the skill level of peers. Then, new human-like controllers, namely, co3-robot controllers where the suffix co3 refers to robots endowed with collaborative, competitive, and cooperative human-like interactive motor behaviors, will be synthesized based on a force-impedance adaptation model and a neural network feedback error learning model of interacting peers. Finally, the co3-robot controller will be implemented to a lower limb exoskeleton and will be validated by passing a haptic Turing test, showing that the controller is indistinguishable from a human partner during dyadic physical interactions. As a result of this research, this work has the potential to enhance existing tools and devices with a haptic communication modality, thus supporting joint physical action between humans and robots.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.

类人机器人在公共工作空间与人类共同执行任务，在家庭和办公室服务、康复和技能培训以及能力增强方面的应用越来越多。数字技术使这些无处不在的机器人能够更容易、更快地进行类似人类的视听交互。然而，缺乏物理交互对人机共享任务构成了重大障碍，这在共享任务期间需要复杂物理交互的应用程序中是必不可少的。在过去的十年里，人们对人类两对或成对之间的物理相互作用进行了大量的研究。几项研究表明，与单独工作相比，二元身体互动可以提高两个人在共同工作时的表现。令人惊讶的是，结果显示，人类二人组不仅表现得更好，而且学习新任务的速度也更快。因此，了解人类的物理互动方式可以应用于开发类人机器人，在与其他人类或机器人执行共享任务时模仿人类行为。这项工作的愿景是了解人类二元物理相互作用的潜在机制，从而改善运动性能和学习率，并将这些知识整合到开发新的机器人控制器中。此外，控制器的基础设施和发现将作为开源共享，并将开发教育计划以支持该领域的进步。有许多因素定义了二人组之间的物理互动，如互动行为（即协作、竞争、合作）、触觉连接（即阻抗水平）和二人组的技能水平（即新手-新手和专家-新手）。因此，能够定量比较每种情况的系统方法至关重要。为了实现这项工作的愿景，将实施一种新的基于外骨骼的二元交互基础设施，通过在外骨骼系统之间提供可变和可控阻抗的虚拟连接，来研究具有多自由度和多接触点的物理二元交互。该基础设施将用于揭示在二元触觉交互中同伴的任务表现和运动学习如何受到1)物理交互行为，2)多关节虚拟连接阻抗以及3)同伴技能水平的影响的综合知识。然后，基于力阻抗自适应模型和交互同伴的神经网络反馈误差学习模型，合成新的类人控制器，即co3-机器人控制器，其中后缀co3指具有协作、竞争和合作类人交互运动行为的机器人。最后，co3机器人控制器将被实现到下肢外骨骼，并将通过触觉图灵测试进行验证，表明控制器在二元物理交互过程中与人类伴侣无法区分。作为这项研究的结果，这项工作有可能通过触觉通信方式增强现有的工具和设备，从而支持人类和机器人之间的联合物理行动。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Framework for Dyadic Physical Interaction Studies during Ankle Motor Tasks

踝关节运动任务期间二元物理相互作用研究的框架

• DOI: 10.1109/lra.2021.3092265
• 发表时间: 2021
• 期刊: IEEE robotics automation letters
• 作者: Kim, Sangjoon J;Wen, Yue;Kucuktabak, Emek Baris;Zhan, Shaobo;Lynch, Kevin;Hargrove, Levi;Perreault, Eric;Pons, Jose L
• 通讯作者: Pons, Jose L

CANopen Robot Controller (CORC): An Open Software Stack for Human Robot Interaction Development

CANopen 机器人控制器 (CORC)：用于人机交互开发的开放软件堆栈

• DOI: 10.1007/978-3-030-69547-7_47
• 发表时间: 2021
• 期刊: WeRob 2020: Wearable Robotics: Challenges and Trends
• 作者: Fong, Justin;Kucuktabak, Emek Baris;Crocher, Vincent;Yan, Ying;Lynch, Kevin M.;Pons, Jose L.;Oetomo, Denny
• 通讯作者: Oetomo, Denny

Effect of Dyadic Haptic Collaboration on Ankle Motor Learning and Task Performance

• DOI: 10.1109/tnsre.2022.3225759
• 发表时间: 2023-01-01
• 期刊: IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
• 影响因子: 4.9
• 作者: Kim，Sangjoon J. J.;Wen，Yue;Pons，Jose L. L.
• 通讯作者: Pons，Jose L. L.