CPS: Synergy: Collaborative Research: Learning control sharing strategies for assistive cyber-physical systems

CPS：协同：协作研究：辅助网络物理系统的学习控制共享策略

• 批准号: 1544797
• 负责人: Siddhartha Srinivasa
• 金额: $ 43.59万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2017-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1544797&HistoricalAwards=false
• 关键词: CPS; Synergy; Collaborative; Research; Learning; CPS; Synergy; Collaborative; Research; Learning

CPS: Synergy: Collaborative Research: Learning control sharing strategies for assistive cyber-physical systemsAssistive machines - like powered wheelchairs, myoelectric prostheses and robotic arms - promote independence and ability in those with severe motor impairments. As the state- of-the-art in these assistive Cyber-Physical Systems (CPSs) advances, more dexterous and capable machines hold the promise to revolutionize ways in which those with motor impairments can interact within society and with their loved ones, and to care for themselves with independence. However, as these machines become more capable, they often also become more complex. Which raises the question: how to control this added complexity? A new paradigm is proposed for controlling complex assistive Cyber-Physical Systems (CPSs), like robotic arms mounted on wheelchairs, via simple low-dimensional control interfaces that are accessible to persons with severe motor impairments, like 2-D joysticks or 1-D Sip-N-Puff interfaces. Traditional interfaces cover only a portion of the control space, and during teleoperation it is necessary to switch between different control modes to access the full control space. Robotics automation may be leveraged to anticipate when to switch between different control modes. This approach is a departure from the majority of control sharing approaches within assistive domains, which either partition the control space and allocate different portions to the robot and human, or augment the human's control signals to bridge the dimensionality gap. How to best share control within assistive domains remains an open question, and an appealing characteristic of this approach is that the user is kept maximally in control since their signals are not altered or augmented. The public health impact is significant, by increasing the independence of those with severe motor impairments and/or paralysis. Multiple efforts will facilitate large-scale deployment of our results, including a collaboration with Kinova, a manufacturer of assistive robotic arms, and a partnership with Rehabilitation Institute of Chicago. The proposal introduces a formalism for assistive mode-switching that is grounded in hybrid dynamical systems theory, and aims to ease the burden of teleoperating high-dimensional assistive robots. By modeling this CPS as a hybrid dynamical system, assistance can be modeled as optimization over a desired cost function. The system's uncertainty over the user's goals can be modeled via a Partially Observable Markov Decision Processes. This model provides the natural scaffolding for learning user preferences. Through user studies, this project aims to address the following research questions: (Q1) Expense: How expensive is mode-switching? (Q2) Customization Need: Do we need to learn mode-switching from specific users? (Q3) Learning Assistance: How can we learn mode-switching paradigms from a user? (Q4) Goal Uncertainty: How should the assistance act under goal uncertainty? How will users respond? The proposal leverages the teams shared expertise in manipulation, algorithm development, and deploying real-world robotic systems. The proposal also leverages the teams complementary strengths on deploying advanced manipulation platforms, robotic motion planning and manipulation, and human-robot comanipulation, and on robot learning from human demonstration, control policy adaptation, and human rehabilitation. The proposed work targets the easier operation of robotic arms by severely paralyzed users. The need to control many degrees of freedom (DoF) gives rise to mode-switching during teleoperation. The switching itself can be cumbersome even with 2- and 3-axis joysticks, and becomes prohibitively so with more limited (1-D) interfaces. Easing the operation of switching not only lowers this burden on those already able to operate robotic arms, but may open use to populations to whom assistive robotic arms are currently inaccessible. This work is clearly synergistic: at the intersection of robotic manipulation, human rehabilitation, control theory, machine learning, human-robot interaction and clinical studies. The project addresses the science of CPS by developing new models of the interaction dynamics between the system and the user, the technology of CPS by developing new interfaces and interaction modalities with strong theoretical foundations, and the engineering of CPS by deploying our algorithms on real robot hardware and extensive studies with able-bodied and users with sprinal cord injuries.

CPS：协同研究：协作研究：辅助网络物理系统协助机器机器的学习控制共享策略（如动力轮椅，肌电假体和机器人手臂）在患有严重运动障碍的人的能力中促进独立性和能力。随着这些辅助网络物理系统（CPSS）的进步中的状态，更灵巧，有能力的机器具有革命性的诺言，可以彻底改变有运动障碍的人可以在社会和亲人中与亲人相互作用，并以独立性照顾自己。但是，随着这些机器变得越来越有能力，它们通常也变得更加复杂。这提出了一个问题：如何控制这一增加的复杂性？提出了一种新的范式，用于控制复杂的辅助网络物理系统（CPSS），例如安装在轮椅上的机器人臂，通过简单的低维控制界面，这些界面可容纳严重运动障碍的人，例如2-D Joysticks或1-D SIP-N-Puff界面。传统的接口仅覆盖控制空间的一部分，在远距离运行期间，有必要在不同的控制模式之间切换以访问完整的控制空间。机器人技术自动化可能会被利用，以预测何时在不同的控制模式之间切换。这种方法与辅助领域内的大多数控制共享方法背道而驰，辅助领域内部的控制空间并将不同的部分分配给机器人和人类，或者增加了人类的控制信号以弥合维度差距。如何在辅助域中最好地共享控制仍然是一个空旷的问题，而这种方法的一个吸引人的特征是，由于未更改或增强信号，因此用户保持最大控制。通过增加严重运动障碍和/或瘫痪的人的独立性，公共卫生的影响很大。多项努力将促进我们的结果的大规模部署，包括与辅助机器人武器制造商Kinova的合作，以及与芝加哥康复研究所的合作伙伴关系。该提案介绍了一种基于混合动力学系统理论的辅助模式切换的形式主义，并旨在减轻远程手术高维辅助机器人的负担。通过将该CPS建模为混合动力学系统，可以将辅助建模为对所需成本函数的优化。可以通过部分可观察到的马尔可夫决策过程对系统对用户目标的不确定性进行建模。该模型为学习用户偏好提供了自然的脚手架。通过用户研究，该项目旨在解决以下研究问题：（Q1）费用：模式转换的昂贵？ （Q2）自定义需求：我们是否需要从特定用户学习模式转换？ （Q3）学习帮助：我们如何从用户学习模式转换范例？ （Q4）目标不确定性：在目标不确定性下应如何行动？用户将如何响应？该提案利用团队在操纵，算法开发和部署现实世界机器人系统方面共享专业知识。该提案还利用团队的补充优势来部署先进的操纵平台，机器人运动计划和操纵以及人类机器人的统治，以及从人类示范，控制政策适应和人类康复的机器人学习。拟议的工作针对严重瘫痪的用户更容易的机器人手臂操作。控制许多自由度（DOF）的需求引起了远程运行期间的模式转换。即使使用2轴和3轴操纵杆，开关本身也可能很麻烦，并且在更有限的（1-D）接口方面变得非常过分。减轻切换的操作不仅减轻了已经能够操作机器人武器的人的负担，而且可能对辅助机器人武器目前无法访问的人群开放。这项工作显然是协同的：在机器人操纵，人类康复，控制理论，机器学习，人类机器人相互作用和临床研究的交集中。该项目通过开发系统与用户之间的相互作用动力学的新模型，通过开发具有强大理论基础的新接口和交互方式的技术来解决CP的科学，以及通过在真实的机器人硬件上部署我们的算法，以及与能力强大的sprolinal bigrial损伤的用户一起部署我们的算法，以及CPS的工程。