CAREER: Preventive Robotics: Learning and Adaptation for Predictive Human Robot Symbiosis

部署辅助技术，智能地将体力任务期间肌肉骨骼损伤的风险降至最低，可以提高用户的安全性，并显著降低与慢性疼痛等长期残疾治疗相关的医疗成本。考虑到这一目标，这个CAREER项目将贡献关键的创新，使机器人能够推理与人类合作伙伴共同行动的生物力学安全性。研究将集中在预防性机器人的概念上，这是一种人机协作的新方法，将人类用户的生物力学福祉纳入机器人控制和决策中。康复机器人专注于受伤后的治疗过程，与之相反，预防机器人寻求主动降低受伤的风险。在这方面，一个关键的知识缺口是缺乏支持人机共生的理论基础——人与机器之间健康、物理和双向的互动，这种互动可以在很长一段时间内舒适地持续下去。预防性机器人的主要目标是产生辅助机器人动作，(a)与人类伙伴的动作无缝融合，以实现预期的功能，同时(b)最大限度地减少对人体的生物力学应力。将这两个目标结合起来，将释放机器人技术的新潜力，从而极大地改善公共和职业健康。该项目还将涉及将创新成果转化为开发智能下肢假肢的商业合作伙伴。这项研究与一项针对K-12学生、本科生和研究生以及来自代表性不足群体的学生的教育计划相结合。为此，该项目将开发一个统一的贝叶斯框架，用于使用紧凑的概率和数据驱动方法在多个代理之间建模共生动态。该框架将弥合人类预测建模和共生人机系统预测控制之间的鸿沟。贝叶斯表示将用于推导学习和适应的算法，其中包括人类用户的未来生物力学状态。此外，将引入新的共生控制算法，利用预测的生物力学变量来引导人机交互朝着生物力学安全的运动体制发展。这些控制方法将为具有相互依赖关系的强耦合系统提供新的见解，其中只有一个系统可以被主动控制（例如，辅助装置或假肢）。这种新方法将应用于动力踝关节假体，以预测关节负荷并主动避免高应力。由此产生的假体将有可能显著降低骨关节炎等肌肉骨骼疾病的风险。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。

Deploying assistive technologies that intelligently minimize the risk of musculoskeletal injury during physical tasks could improve user safety and significantly reduce the healthcare costs associated with the treatment of long-term disabilities such as chronic pain. With that goal in mind, this CAREER project will contribute key innovations that allow robots to reason about the biomechanical safety of actions performed jointly with a human partner. The research will focus on the concept of Preventive Robotics, a novel approach to human-machine collaboration that incorporates the biomechanical well-being of the human user into robot control and decision-making. In contrast to Rehabilitation Robotics, which focuses on therapeutic procedures after an injury occurs, Preventive Robotics seeks to proactively reduce the risk of injury. A critical knowledge gap in this regard is the absence of a theoretical foundation that supports human-machine symbiosis - healthy, physical, and bi-directional interactions between human and machine which can be comfortably sustained over very long periods of time. The main objective of Preventive Robotics is to generate assistive robot actions that (a) seamlessly blend with actions of the human partner to achieve the intended function, while (b) minimizing biomechanical stress on the human body. Coalescing these two goals will unlock new potential for robotics to drastically improve public and occupational health. The project will also involve transition of innovations to a commercial partner developing intelligent lower-leg prostheses. The research integrates with an education program targeting K-12 students, undergraduate and graduate students, and students from underrepresented groups. To these ends, the project will develop a unified Bayesian framework for modeling symbiotic dynamics among multiple agents using a compact probabilistic and data-driven methodology. The framework will bridge the divide between predictive modeling of humans and predictive control of symbiotic human-robot systems. A Bayesian representation will be used to derive algorithms for learning and adaptation which include the future biomechanical state of a human user. In addition, new symbiotic control algorithms will be introduced that utilize predicted biomechanical variables to steer the human-robot interaction towards biomechanically safe movement regimes. These control methods will provide new insights about strongly-coupled systems with reciprocal dependencies, in which only one system can be actively controlled (e.g., an assistive device or prosthesis). The new approach will be implemented on a powered-ankle prosthesis in order to anticipate joint loads and proactively avoid high stresses. The resulting prosthesis will have the potential to significantly lower the risk of musculoskeletal diseases such as osteoarthritis.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.