FREEpHRI: Flexible, Robust and Efficient physical Human-robot Interaction with iterative learning and self-triggered role adaption

FREEpHRI：灵活、稳健、高效的物理人机交互，具有迭代学习和自我触发的角色适应能力

• 批准号: EP/V057782/2
• 负责人: Zhenhong Li
• 金额: $ 32.82万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV057782%2F2
• 关键词: FREEpHRI; Flexible; Robust; Efficient; physical

Globally, the number of robots in use in 2020 is over 2.25 million, which will multiply even faster in the next 10 years, reaching 20 million by 2030. The fast adoption of robots has contributed 10% of worldwide total GDP growth in the last five years. The technological advancements are bringing robots to humans' daily lives, and they are no longer working in an isolated environment, but sharing the same workspace and physically interacting with humans, e.g., rehabilitation robots, tele-operation robots and collaborative robots. The physical coupling between humans and robots, often termed as physical human-robot interaction (pHRI), facilitates new human performance capabilities and creates opportunities to explore the task sharing and the control between humans and robots. To maximise the benefit of human-robot system during joint tasks, the robot needs to understand what the human is trying to do, and intelligently adjusts its behaviour according to the performance of the human and the requirements of tasks, which requires novel tools to model the human behaviours and innovate strategies to modulate the control of the robot. The ambition of this fellowship is to enable robots to real-time estimate human behaviours, intelligently detect the changes of human behaviours, automatically adjust the relationship between the human and the robot (from collaborative to competitive), and provide natural interaction behaviours even when the robot dynamics are partly unknown. I will pursue this goal by: 1) developing a flexible and robust pHRI control strategy. The control strategy uses a two-player differential game to model human-robot interaction behaviours, and learning techniques to compensate the effects of unknown dynamics and external disturbances. A cost function implying motor capability will be assigned to the human partner, and the robot will adjust its role (collaborator or competitor) according to the real-time estimation of the human cost function. 2) introducing an efficient self-triggered role adaption mechanism. The triggering mechanism uses the performance of the human-robot system and the estimated human behaviour to detect the role changes of the human, and triggers the robot to change its role when necessary; 3) evaluating the reliability and functionality of the proposed techniques through an exemplar application in physical robot-assisted rehabilitation. The proposed techniques will be used to achieve typical training strategies (e.g., passive, assist-as-needed, challenge-based) initially in laboratory settings, and then in the Leeds Teaching Hospital rehabilitation service. This fellowship targets at two fundamental issues in pRHI: (1) how to efficiently update the robot's control strategy to ensure desired interactions; and (2) how to deal with uncertainties in the human-robot system. The technologies developed in this fellowship will provide a general framework for designing an interactive robot control system, which has a large group of applications in both healthcare and manufacturing. The fellowship objectives and milestones will be delivered collaboratively with partners from the University of Leeds, the University of the West of England Bristol, the University of Manchester, Leeds Teaching Hospitals NHS Trust, Devices for Dignity, YIRUIDE Medical and DIH/Hocoma.

从全球来看，2020年使用的机器人数量将超过225万台，未来10年将增长得更快，到2030年将达到2000万台。机器人的快速采用对过去五年全球GDP增长贡献了10%年。技术进步将机器人带入人类的日常生活，它们不再在孤立的环境中工作，而是共享同一工作空间并与人类进行物理交互，例如康复机器人、远程操作机器人和协作机器人。人类和机器人之间的物理耦合，通常称为物理人机交互（pHRI），促进了新的人类表现能力，并为探索人类和机器人之间的任务共享和控制创造了机会。为了最大限度地发挥人机系统在联合任务中的优势，机器人需要了解人类正在尝试做什么，并根据人类的表现和任务要求智能地调整其行为，这需要新颖的工具来建模人类行为并创新策略来调节机器人的控制。该奖学金的目标是使机器人能够实时估计人类行为，智能检测人类行为的变化，自动调整人类与机器人之间的关系（从协作到竞争），并提供自然的交互行为，即使在机器人动力学部分未知。我将通过以下方式实现这一目标：1) 开发灵活且稳健的 pHRI 控制策略。该控制策略使用两人微分博弈来模拟人机交互行为，并使用学习技术来补偿未知动态和外部干扰的影响。一个暗示运动能力的成本函数将被分配给人类伙伴，机器人将根据人类成本函数的实时估计来调整自己的角色（合作者或竞争对手）。 2）引入高效的自触发角色适配机制。触发机制利用人机系统的性能和估计的人类行为来检测人的角色变化，并在必要时触发机器人改变角色； 3）通过物理机器人辅助康复中的示例应用来评估所提出技术的可靠性和功能性。所提出的技术将用于首先在实验室环境中实现典型的培训策略（例如，被动、按需协助、基于挑战），然后在利兹教学医院康复服务中实现。该奖学金针对 pRHI 中的两个基本问题：（1）如何有效地更新机器人的控制策略以确保所需的交互； (2)如何处理人机系统中的不确定性。该奖学金开发的技术将为设计交互式机器人控制系统提供通用框架，该系统在医疗保健和制造领域都有大量应用。该奖学金的目标和里程碑将与利兹大学、西英格兰大学布里斯托尔分校、曼彻斯特大学、利兹教学医院 NHS Trust、Devices for Dignity、YIRUIDE Medical 和 DIH/Hocoma 的合作伙伴合作实现。

项目成果

An Assistive Wrist-Elbow Exoskeleton (WE-Exo) Driven by Variable Stiffness Actuators

由可变刚度执行器驱动的辅助腕肘外骨骼 (WE-Exo)

• DOI: http://dx.10.1109/m2vip58386.2023.10413391
• 发表时间: 2023
• 作者: Wang C

An EMG-driven musculoskeletal model for estimation of wrist kinematics using mirrored bilateral movement

EMG 驱动的肌肉骨骼模型，用于使用镜像双边运动估计手腕运动学

• DOI: 10.1016/j.bspc.2022.104480
• 发表时间: 2023-03-01
• 期刊: Biomed. Signal Process. Control.
• 作者: Yihui Zhao;Zhenhong Li;Zhi;Kun Qian;Shengquan Xie
• 通讯作者: Shengquan Xie

Physics-Informed Deep Learning for Musculoskeletal Modeling: Predicting Muscle Forces and Joint Kinematics From Surface EMG

用于肌肉骨骼建模的物理信息深度学习：根据表面肌电图预测肌肉力和关节运动学

• DOI: http://dx.10.1109/tnsre.2022.3226860
• 发表时间: 2023
• 期刊: IEEE Transactions on Neural Systems and Rehabilitation Engineering
• 影响因子: 4.9
• 作者: Zhang J

A Novel Series Elastic Actuator with Variable Stiffness

新型系列变刚度弹性执行器

• DOI: 10.1109/aim46323.2023.10196237
• 发表时间: 2023-06-28
• 期刊: 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
• 作者: Chao Wang;Zhenhong Li;Bo Sheng;M. Sivan;Zhi;Guqiang Li;Shengquan Xie
• 通讯作者: Shengquan Xie

Adaptive Cooperative Control Strategy for a Wrist Exoskeleton Using Model-Based Joint Impedance Estimation

使用基于模型的关节阻抗估计的手腕外骨骼自适应协作控制策略

• DOI: 10.1109/tmech.2022.3211671
• 发表时间: 2023-04-01
• 期刊: IEEE/ASME Transactions on Mechatronics
• 作者: Yihui Zhao;Kun Qian;Sheng Bo;Zhi;Zhenhong Li;Guqiang Li;A. Dehghani;Shengquan Xie
• 通讯作者: Shengquan Xie