CAREER: Facilitating Human Interaction with Assistive Robots Through Intent Signaling and Inference

这项教师早期职业发展计划（Career）资助将开发一个新的博弈论框架，解决人机物理交互过程中联合意图推理的新挑战。物理人机交互在协同制造、机器人辅助康复和机器人手术中已经无处不在。然而，大多数现有的机器人缺乏高水平的智能来推理、适应和潜在地在协作任务的背景下塑造人类的行为。这个跨学科的项目融合了博弈论、最优控制方法和从认知层次理论中收集的想法，以解决用于步态康复的动力膝关节外骨骼范例应用中的关节推理问题。本项目将通过开发一种新的控制框架来促进科学进步和国民健康，该框架将使具身机器智能能够理解嘈杂的人类运动动作，从而促进人类对运动任务的学习。这样做，这个项目将改善用户体验和未来人机团队的生产力。通过开发人机交互的新课程，通过与医疗合作伙伴的联合研讨会，以及为科学、技术、工程和数学（STEM）教师提供暑期实习，该项目的影响将得到扩大。该项目解决了人机物理交互过程中有关联合意图推理的重大挑战。该应用是用于步态康复的动力膝关节外骨骼。该项目进行三个具体的研究活动。第一种方法是创建一个人类与机器人进行身体互动时的认知状态模型。该模型明确考虑了人机交互动力学、相互学习和多层次意图推理。第二部分提出了一个运动规划和控制问题，这将使机器人既能发出其意图的信号，又能基于已识别的人类认知和生物力学模型促进人类运动学习。第三个将量化人类表现的可变性，并用它来提高意图推理和信号性能。人体实验将提供建立人类认知状态（包括意图）模型所需的数据，以证明与现有机器人控制器相比，人机团队的表现有所改善，并有助于理解有效的人机物理交互的基本机制。该项目的一个关键创新是创建了一个任务不可知论框架，该框架利用人类认知和运动动力学模型，使智能机器可以动态调整其行为，同时促进人类学习并在需要时提供物理帮助。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。

This Faculty Early Career Development Program (CAREER) grant will develop a novel game-theoretic framework addressing the emerging challenge of joint intent inference during physical human/machine interaction. Physical human-machine interaction has become ubiquitous in collaborative manufacturing, robot-aided rehabilitation, and robotic surgery. However, most existing robots lack high-level intelligence to reason about, adapt to, and potentially shape human actions within the context of collaborative tasks. This interdisciplinary project merges game theory, optimal control methods, and ideas gleaned from cognitive hierarchy theory to address the joint inference problem within the exemplar application of a powered knee exoskeleton used for gait rehabilitation. This project will promote the progress of science and advance the national health by developing a novel control framework that will enable an embodied machine intelligence to understand noisy human motor actions, thereby facilitating human learning of motor tasks. In doing so, this project will improve user experience and the productivity of future human-robot teams. The impact of this project will be broadened by developing new curriculum on human-robot interaction, through joint workshops with healthcare partners, and by offering summer internships for Science, Technology, Engineering, and Math (STEM) teachers.This project addresses significant challenges pertaining to joint intent inference during human-machine physical interaction. The application is that of a powered knee exoskeleton used for gait rehabilitation. The project pursues three specific research activities. The first creates a model of the human's cognitive state when interacting physically with a robot. The model explicitly considers human-machine interaction dynamics, mutual learning, and multi-level intent inference. The second formulates a motion planning and control problem, which will allow the robot both to signal its intents and to facilitate human motor learning based on the identified cognitive and biomechanical models of the human. The third will quantify human performance variability and use it to improve intent inference and signaling performance. Human subject experiments will provide the data needed to build the model of human cognitive state (including intent), to demonstrate improved human-robot team performance over existing robot controllers, and to help understand fundamental mechanisms contributing to effective physical human-robot interaction. A key innovation of this project is the creation of a task-agnostic framework that leverages models of human cognitive and motor dynamics such that an intelligent machine can dynamically adjust its behavior to simultaneously facilitate human learning and provide physical assistance when needed.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.