CAREER: Human-Centric Control for Teleoperated Surgical Robots

人在环控制策略使用户更好地执行任务，并对此更有信心，是遥操作机器人研究的一个重要领域。然而，人类的行为往往会因环境、身体、情感和社会因素而发生变化。该学院早期职业发展计划(Career)项目的目标是设计能够解释和响应动态人类用户的自适应控制系统。这项研究将在外科机器人领域产生影响，在该领域，与患者的互动需要人类操作员(外科医生)和机器人系统本身的安全性和有效性。意识到用户技能和性能风格并对其做出响应的机器人系统可以更好地避免用户错误，并在不可预测的环境中对不利事件做出反应。通过将用户意图、运动风格和专业知识水平的实时模型与手术机器人平台相集成，该项目将推进NSF的使命，通过探索手术机器人环境中人类行为、运动控制和机器操作的基本关系来促进科学进步和促进国民健康。该项目通过促进与医疗保健相关的创新活动以及用于医学模拟和培训的技术开发，支持教育和扩大对工程的参与。这个职业项目的目标是为远程操作的机器人手术系统开发自适应控制算法，该算法可以根据以用户为中心的行为和任务难度模型的输出来响应、忽略和/或增加人类运动控制输入。模型输出将基于对人类意图、手术风格和专业知识水平的实时、数据驱动的预测和解释。研究目标包括：开发方法来模拟和控制非结构化遥操作任务中的人类行为(例如，用户行为和专业知识)；设计和分析自适应控制律以通过视觉和触觉指导来提高性能；以及评估这些算法在使用外科机器人平台的训练和干预中对临床相关结果的有效性。这项工作的一个关键创新是设计了与人类操作员执行的特定任务无关的控制方法：只有以用户为中心的度量和运动数据将作为新的难度和风格预测模型的输入，然后这些模型将被用于创建自适应控制算法。这项工作可以在与人类用户合作时显著提高远程手术系统的适应性、能力和可用性。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。

Human-in-the-loop control strategies in which the user performs a task better, and feels more confident to do so, is an important area of research in teleoperated robotics. However, human behavior can often change as a result of environmental, physical, emotional, and social factors. The goal of this Faculty Early Career Development Program (CAREER) project is to design adaptive control systems that can interpret and react to the dynamic human user. This research will be impactful in the field of surgical robotics where interaction with the patient demands safety and effectiveness from both the human operator (surgeon) and the robotic system itself. Robotic systems that are aware and responsive to user skill and performance style could be more able to avoid user errors and respond to adverse events in unpredictable environments. By integrating real-time models of user intent, movement style, and expertise level with a surgical robotic platform, this project will advance the NSF mission to promote the progress of science and advance national health by exploring fundamental relationships human behavior, motor control, and machine manipulation within the context of surgical robotics. The project supports education and broadening participation in engineering by promoting innovation activities related to healthcare and technology development for medical simulation and training. The goal of this CAREER project is to develop adaptive control algorithms for teleoperated robotic surgical systems that can respond to, ignore, and/or augment human motor control inputs depending on the output of user-centric models of behavior and task difficulty. Model output will be based in real-time, data-driven predictions and interpretations of human intent, surgical style, and level of expertise. Research objectives include: developing methods to model and control human behavior (e.g., user behavior and expertise) during unstructured teleoperation tasks; designing and analyzing adaptive control laws to enhance performance through visual and haptic guidance; and evaluating the effectiveness of these algorithms on clinically-relevant outcomes in training and intervention using a surgical robotic platform. A key innovation of this work is designing control methods to be agnostic of the specific task performed by the human operator: only user-centric metrics and movement data will serve as inputs to novel difficulty and stylistic prediction models that will then be used to create adaptive control algorithms. This work could lead to significant improvements in the adaptability, capability, and usability of teleoperated surgical systems when collaborating with a human user.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.