CAREER: Robot Learning from Motor-Impaired Instructors and Task Partners

辅助机器--如电动轮椅、机械臂、外骨骼、假肢--对于有严重运动障碍的人的独立性至关重要。然而，存在着一个悖论，一个人的损伤越严重，他们操作这些可能提高他们生活质量的机器的能力就越差。在这里，机器人技术具有改变人类健康和康复领域的潜力：通过将机器转变为机器人，可以自主操作并分担控制负担。至关重要的是，这些机器人要适应人类用户的独特偏好和能力，而这两者如何随着时间的推移而变化，对于实现广泛的采用和接受至关重要，特别是如果连接到人类身体上提供物理帮助的话。从非专家那里学习机器人的研究有限，而运动障碍教师的领域更具挑战性：他们的控制信号噪声(由于运动信号中的伪影)和稀疏(如果提供运动命令更困难)，并通过接口进行过滤。这项拟议的工作没有将这些约束视为限制，而是假设这些约束对利用来自运动障碍患者的控制和反馈信号的独特特征(如问题空间稀疏性)的机器学习算法是有利的。这项工作开发了多种新的机器学习算法技术，(1)明确地解释控制界面以及它如何与整个机器人控制空间交互；(2)从人类遥操作命令的可变性中得出关于人类控制模式和任务要求的信息；以及(3)其中包括设计适应线索，这些线索来自运动障碍教师的基于奖励和基于实例的反馈。拟议的工作还对操作多个机器人平台的运动受损最终用户进行主题研究，以探索这一问题空间，并评估贡献的算法技术的功能和用户接受度。

Assistive machines - like power wheelchairs, robotic arms, exoskeletons, prostheses - are vital for enabling independence for people with severe motor impairments. However, there exists a paradox, where often the more severe a person's impairment, the less able they are to operate these very machines which might improve their quality of life. Here robotics technologies have the potential to transform the field of human health and rehabilitation: by turning the machine into a robot, that can operate itself autonomously and share the control burden. It will be crucial that these robots adapt to the human user's unique preferences and abilities, and how both change over time is crucial for achieving widespread adoption and acceptance, and especially if attached to a human's body to provide physical assistance. There has been limited study of robot learning from non-experts, and the domain of motor-impaired teachers is even more challenging: their control signals are noisy (due to artifacts in the motor signal) and sparse (if providing motor commands is more effort), and filtered through an interface. Rather than treat these constraints as limitations, the proposed work hypothesizes that such constraints become advantageous for machine learning algorithms that exploit unique characteristics (like problem-space sparsity) of control and feedback signals from motor-impaired humans. The work develops multiple novel machine learning algorithmic techniques, (1) that reason explicitly about the control interface and how it interacts with the full robot control space; (2) that derive information about the human's control patterns and task requirements, from variability in the human's teleoperation commands; and (3) which include the design of adaptation cues informed by reward- and example-based feedback from motor-impaired teachers. The proposed work also performs subject studies with motor-impaired end-users operating multiple robotic platforms, both to explore this problem space and assess the functionality and user acceptance of the contributed algorithmic techniques.