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辅助机器--如电动轮椅、肌电假肢和机械臂--促进严重运动障碍者的独立性和能力。随着这些辅助网络物理系统(CPSS)中最先进的技术的进步，更灵活、更有能力的机器有望彻底改变运动障碍者在社会内部和与他们所爱的人互动的方式，并以独立的方式照顾自己。然而，随着这些机器变得更有能力，它们往往也变得更加复杂。这就提出了一个问题：如何控制这种增加的复杂性？提出了一种新的范式，通过简单的低维控制界面来控制复杂的辅助数字物理系统(CPSS)，如安装在轮椅上的机械臂，有严重运动障碍的人可以访问这些界面，如2-D操纵杆或1-D sip-N-Puff接口。传统的界面只覆盖了控制空间的一部分，在遥操作过程中，需要在不同的控制模式之间切换才能访问整个控制空间。可以利用机器人自动化来预测何时在不同控制模式之间切换。这种方法与辅助域中的大多数控制共享方法不同，这些方法要么划分控制空间并将不同的部分分配给机器人和人类，要么增强人类的控制信号来弥合维度差距。如何在辅助域内最好地共享控制仍然是一个悬而未决的问题，这种方法的一个吸引人的特点是，由于用户的信号没有改变或增强，所以用户被保持在最大限度的控制中。通过增加严重运动障碍和/或瘫痪患者的独立性，对公共卫生的影响是显著的。多方面的努力将促进我们成果的大规模部署，包括与辅助机械臂制造商Kinova的合作，以及与芝加哥康复研究所的合作。该方案引入了一种基于混合动力系统理论的辅助模式切换的形式化方法，旨在减轻遥操作高维辅助机器人的负担。通过将该CPS建模为混合动态系统，可以将辅助建模为对期望代价函数的优化。系统对用户目标的不确定性可以通过部分可观测的马尔可夫决策过程来建模。该模型为学习用户偏好提供了天然的脚手架。通过用户研究，该项目旨在解决以下研究问题：(第一季度)费用：模式切换有多贵？(Q2)定制需求：我们是否需要向特定用户学习模式切换？(Q3)学习帮助：我们如何从用户那里学习模式切换范例？(Q4)目标不确定性：在目标不确定性下，援助应如何行动？用户会作何反应？该提案利用了团队在操纵、算法开发和部署真实世界机器人系统方面的共享专业知识。该提案还利用两个团队在部署先进操纵平台、机器人运动规划和操纵、人-机器人协同操纵、机器人从人类演示中学习、控制政策适应和人类康复方面的互补优势。这项拟议的工作旨在让严重瘫痪的用户更容易操作机械臂。在遥操作过程中，控制多个自由度(DoF)的需要引起了模式切换。即使使用2轴和3轴操纵杆，切换本身也可能很麻烦，而对于更有限的(1-D)接口，切换变得令人望而却步。简化切换操作不仅可以减轻那些已经能够操作机械臂的人的负担，还可以向目前无法接触到辅助机械臂的人群开放使用。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-机器人交互和临床研究的交叉点上。该项目通过开发系统和用户之间的交互动力学的新模型来解决CPS的科学，通过开发具有强大理论基础的新界面和交互模式来解决CPS的技术，通过在真实的机器人硬件上部署我们的算法和对健全的和患有脊髓损伤的用户进行广泛的研究来解决CPS的工程问题。

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.