NRI: Small: Modeling, Quantification, and Optimization of Prosthesis-User Interface

建议编号：1317379问题描述：更好的机器人假肢可以极大地改善40,000多名接受上肢截肢手术的美国人的生活质量，他们中的许多人拒绝现有的设备，因为他们很难用控制完整手臂的直观、潜意识的方式来控制它们。假肢控制是困难的，因为截肢者在他们的设备是否会对他们的控制信号做出适当反应以及感觉反馈信号是否准确地反映实际运动方面都经历了巨大的不确定性。研究人员一直专注于改善控制的孤立方面，例如通过改进过滤器或通过触觉设备模仿健全的感觉提示，但这些方法最大限度地减少了假肢控制的不确定性。人与假肢的相互作用是一个多方面的、时变的问题，很难解决。机器人假肢研究中缺少的是优化控制策略和感觉线索的原则性方法，这些方法考虑了人们在面对高度不确定性时所做出的行为选择。智力价值：拟议的研究具有创新性，因为它在更广泛的背景下提出了协作机器人问题，其中包含了人类在优化其控制策略和感觉线索时做出的高度复杂的行为决策。这种有原则的方法能够以以前的方法所不可能的方式整合多种效果。例如，建议的方法自然结合了这样一个事实，即人们更喜欢使用较少的努力来完成一项任务，但在需要更高精度的运动部分(如轨迹的最后部分)可以容忍更多的努力。另一方面，如果高确定性触觉线索为现有的感官线索(如视觉)提供了冗余信息，或者如果触觉信息不能减少可控系统动力学的不确定性，则该方法不支持高确定性触觉线索。由于截肢者存在较大的控制信号噪声源，拟议的工作将导致计算电机控制和最优控制领域的技术改进。这项研究建立在S团队在上肢假体设计和控制以及开发计算机运动控制领域的丰富经验的基础上。提出的目标的实现将有助于机器人控制领域以及人-机器人交互、感知、操作和外骨骼等不同领域的发展。更广泛的影响：除非对人类如何在相互作用的不确定源面前与这些合作机器人整合有一个明确的理解，否则真正的仿生假肢、外骨骼和类人机器人控制是不可能的。这一计算马达项目将为人类如何在存在巨大不确定性的情况下控制运动提供变革性的见解，从而填补该领域知识库中的一个关键空白。这项研究开发的框架将引起运动控制研究界的极大兴趣，并可能有助于恢复其他运动障碍，如脊髓损伤和中风。这项提案的牵头机构芝加哥康复研究所(RIC)一直被评为全国最好的康复医院。RIC内部的研究和临床卓越非常接近，这确保了这项工作产生的好处将迅速传播给假体使用者。研究团队还将寻求更广泛的受众--RIC的实验室经常有来自当地高中和大学的学生参观，RIC还为芝加哥市中心的外展活动做出了贡献。这些外展计划促进了康复研究的意识和追求工程事业的热情。此外，该团队将以西北大学和S皇后大学开发的成功的计算感觉-运动神经科学暑期学校的模板为基础，开发一个K-12教育模块，该模块将提供理论和学生主导的实验相结合的游戏，这些游戏将满足伊利诺伊州在科学、数学和英语语言艺术方面的许多学习标准。

PI: Sensinger, J. W.; Hargrove, L.; and Kording, K. P.Proposal Number: 1317379Problem Description: Better robotic prostheses can dramatically improve the quality of life for the more than 40,000 Americans with an upper limb amputation, many of whom reject existing devices because they have trouble controlling them in the same intuitive, subconscious way that they controlled their intact arms. Prosthesis control is difficult because amputees experience great uncertainty both with respect to whether their device will respond appropriately to their control signals and whether sensory feedback cues accurately reflect the actual movement. Researchers have focused on improving isolated aspects of control, for example by improving filters or mimicking able-bodied sensory cues through haptic devices, but these approaches have minimally reduced the uncertainty of prosthesis control. Human interaction with a prosthesis is a multifaceted, time-varying problem that is difficult to solve. What is missing from robotic prosthesis research are principled methods for optimizing control strategies and sensory cues which take into account behavioral choices people are known to make in the face of high uncertainty.Intellectual Merit: The proposed research is innovative because it poses the co-robot problem in a broader context that incorporates the highly sophisticated behavioral decisions that humans make in optimizing their control strategy and sensory cues. This principled approach is able to integrate multiple effects in ways that were not possible using previous approaches. For example, the proposed approach naturally incorporates the fact that people prefer to use less exerted effort to accomplish a task, but tolerate more effort during portions of movement that require greater precision (e.g. final portion of a trajectory). On the other hand, the approach does not favor high-certainty haptic cues if those cues provide redundant information to existing sensory cues such as vision, or if the haptic information does not reduce the uncertainty of controllable system dynamics. Due to the large sources of control-signal noise present in amputees, the proposed work will lead to improved techniques within the fields of computational motor control and optimal control. This research builds on the team?s extensive experience in the design and control of upper-limb prostheses and in developing the field of computational motor control. Achievement of the proposed aims will contribute to the field of robotic control and to such diverse fields as human-robot interaction, perception, manipulation, and exoskeletons.Broader Impacts: True biomimetic prostheses, exoskeletons, and humanoid robot control will not be possible until there is a firm understanding of how humans integrate with these co-robots in the face of interacting sources of uncertainty. This computational motor project will provide transformative insight into how humans control movement in the presence of large uncertainty and thus fill a critical gap in the knowledge base of this field. The framework developed in this research will be of great interest to the motor-control research community and may be useful in the restoration of other movement disorders such as spinal cord injury and stroke. The lead institution of this proposal, the Rehabilitation Institute of Chicago (RIC), is consistently ranked the top rehabilitation hospital in the country. The close proximity of research and clinical excellence within RIC ensures that the benefits resulting from this work will be quickly disseminated to prosthesis users. The research team will also seek to reach a broader audience?the laboratories at the RIC are regularly visited by students from local high schools and universities, and the RIC also contributes to outreach activities within inner-city Chicago. These outreach programs promote an awareness of rehabilitation research and an enthusiasm for pursuing a career in engineering. Additionally, the team will develop a K-12 educational module based on the template of the successful Summer School in Computational Sensory-Motor Neuroscience developed at Northwestern University and Queen?s University, which will provide a combination of theory and student-driven experimentation using games that will address many of the Illinois Learning Standards in science, math, and English language arts.