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Collaborative Research: Identifying Model-Based Motor Control Strategies to Enhance Human-Machine Interaction

协作研究：确定基于模型的电机控制策略以增强人机交互

基本信息

批准号：
1825489
负责人：
Noah Cowan
金额：
$ 38.15万
依托单位：
Johns Hopkins University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1825489&HistoricalAwards=false
关键词：
Collaborative Research Identifying Model Based

项目摘要

Humans are increasingly asked to cooperate with machines and robots in many occupational and recreational settings, including teleoperation, driving vehicles, surgery, rehabilitation, and object manipulation. Not only must humans learn to share control with a machine, the machines must in turn be better designed to enable human-machine interaction. The main objectives of this collaborative project are: to perform fundamental research that will identify multisensory (visual and haptic), human-in-the-loop, sensorimotor control models that capture predictive and reactive aspects of how people interact with their physical environment (e.g., machines); to advance understanding of how multisensory control can degrade in a patient population with degeneration of the cerebellum, which is thought to contribute importantly to tool use; and to advance understanding of how control systems for machines can be developed to exploit identified models of human sensorimotor control to enhance performance of human-machine interactions. The project is significant because it develops computable theories (computational models of human sensorimotor control) and the physical manifestation of those theories (robotic control algorithms) that will lead to enhanced human-machine interactions. This project directly serves the NSF mission by promoting fundamental science exploring modes of interaction between humans and intelligent robotic systems, which may contribute to advancing the national health. The project supports education through outreach activities aimed at recruiting and retaining students in STEM fields.This research will contribute to a fundamental understanding of human motor behavior by developing a set of multidomain (haptic and visual) models that describe the application of model-based control strategies in the context of accommodating (or rejecting) influences from the environment. Aim 1 builds on the assumption that the computational problem solved by the human nervous system can be captured using model-based control strategies involving a combination of predictive (feedforward) and reactive (feedback) mechanisms. In a series of four sets of experiments, the team will use single-sine "predictable" and sum-of-sines "unpredictable" disturbances of visual and haptic feedback to interrogate sensorimotor control during reaching and object manipulation tasks. By identifying the structure and parameters of neuromotor control in these tasks, the PIs set the stage for later development of engineered control systems to improve human-machine interaction. Experiments supporting Aim 2 will mirror those serving Aim 1, identifying how sensorimotor control is impaired in a cohort of cerebellar ataxia patients. Expected results promise insight into the cerebellum's contributions to motor coordination and control, thereby advancing the national research priority of understanding brain function in health and disease. Aim 3 seeks to engineer intelligent machine controllers to "wrap around" a human's model-based control system to enhance cooperative performance of the overall human-machine system. Cohorts of neurologically intact and cerebellar patients will be tested. One set of experiments will examine the extent to which human participants can correctly interpret haptic feedback to correctly perceive whether a coupled automaton works "for" or "against" their efforts. A second set of experiments will exploit individualized models of sensorimotor control to examine the extent to which real-time visual feedback of hand position can be augmented to enhance performance of goal-directed reaching in patients with cerebellar ataxia. The project outcomes may have long-term impact by advancing understanding of how machine control can be designed to enhance performance of physically-coupled human-machine systems.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.

在许多职业和娱乐环境中，人类越来越多地被要求与机器和机器人合作，包括远程操作、驾驶车辆、手术、康复和物体操纵。人类不仅要学会与机器共享控制，机器也必须进行更好的设计，以实现人机交互。这个合作项目的主要目标是：进行基础研究，将确定多感官（视觉和触觉），人在环，感觉运动控制模型，捕捉人们如何与他们的物理环境（例如，机器）;为了进一步了解多感觉控制如何在小脑退化的患者群体中退化，这被认为对工具的使用有重要贡献;并进一步了解如何开发机器控制系统，以利用人类感觉运动控制的识别模型来增强人机交互的性能。该项目意义重大，因为它开发了可计算理论（人类感觉运动控制的计算模型）和这些理论的物理表现（机器人控制算法），这将导致增强人机交互。该项目直接服务于NSF的使命，促进基础科学探索人类与智能机器人系统之间的互动模式，这可能有助于促进国民健康。该项目通过旨在招募和留住STEM领域学生的推广活动支持教育。该研究将通过开发一套多领域（触觉和视觉）模型来描述基于模型的控制策略在适应（或拒绝）环境影响的背景下的应用，从而有助于对人类运动行为的基本理解。目标1建立在这样的假设上，即由人类神经系统解决的计算问题可以使用基于模型的控制策略来捕获，该控制策略涉及预测（前馈）和反应（反馈）机制的组合。在一系列的四组实验中，研究小组将使用单正弦“可预测”和西内斯“不可预测”的视觉和触觉反馈干扰，以询问在达到和对象操作任务期间的感觉运动控制。通过识别这些任务中神经运动控制的结构和参数，PI为以后开发工程控制系统以改善人机交互奠定了基础。支持目标2的实验将反映目标1的实验，确定小脑性共济失调患者的感觉运动控制是如何受损的。预期结果有望深入了解小脑对运动协调和控制的贡献，从而推进了解健康和疾病中大脑功能的国家研究重点。目标3寻求设计智能机器控制器来“包裹”基于人类模型的控制系统，以增强整个人机系统的合作性能。将对神经系统完整和小脑患者队列进行检测。一组实验将检查人类参与者能够正确解释触觉反馈的程度，以正确感知耦合自动机是否“支持”或“反对”他们的努力。第二组实验将利用个体化的感觉运动控制模型，以研究在何种程度上可以增强手的位置的实时视觉反馈，以提高小脑性共济失调患者的目标导向达到的性能。项目成果可能会产生长期影响，因为它促进了人们对如何设计机器控制以增强物理耦合人机系统性能的理解。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值进行评估而被认为值得支持。和更广泛的影响审查标准。