Learning-based Adaptive Optimal Control Principles for Human Movements

基于学习的人体运动自适应最优控制原理

基本信息

批准号：
1903781
负责人：
Zhong-Ping Jiang
金额：
$ 29.36万
依托单位：
New York University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1903781&HistoricalAwards=false
关键词：
Learning based Adaptive Optimal Control

项目摘要

The study of human movement, driven by the desire to expand our understanding of the brain, holds great promise for devising new therapies for people who suffer from neurodegenerative disorders such as Parkinson's disease and Huntington's disease. This proposal aims to deepen our preliminary research in learning-based control theory as a new computational principle of sensorimotor control, while, at the same time, validating the proposed theory through numerical simulations and biological experiments. It is an interdisciplinary project that combines tools and methods from reinforcement learning, nonlinear control theory, and adaptive dynamic programming. Rigorous stability and robustness analysis as well as convergence proofs for the proposed learning algorithms will be carried out. A fundamentally novel aspect of the proposal is that attention will be paid to continuous-time dynamical systems described by differential equations as opposed to the conventional models used in the past literature such as discrete-time systems and Markov decision processes (MDPs). Intellectual merit:This interdisciplinary project is aimed at developing and validating robust adaptive dynamic programming as a theory of human sensorimotor learning and control. To this end, there is a great need to develop new results that go beyond the present literature by considering a wider class of continuous-time stochastic systems with both additive and multiplicative noise and strong nonlinearities. Human behavioural experiments are to be designed to test and develop computational models of sensorimotor control. Broader impacts:Even though human movement problems are targeted in this project, it is expected that the findings of this project toward learning-based control theory will be useful for other problems arising from engineering and computational neuroscience such as robotic rehabilitation. The proposal also includes a plan of educational activities centered on student supervision, curriculum development, knowledge dissemination, and collaboration with New York University Center for Neural Science. The PI hopes to organize workshops and invited sessions at major conferences, providing an opportunity to students and junior researchers to interact with leading authorities in automatic control and computational neuroscience.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.

为了扩大我们对大脑的了解，对人体运动的研究有望为患有帕金森病和亨廷顿舞蹈病等神经退行性疾病的人们设计新疗法。该提案旨在深化基于学习的控制理论作为感觉运动控制的新计算原理的初步研究，同时通过数值模拟和生物实验验证所提出的理论。它是一个跨学科项目，结合了强化学习、非线性控制理论和自适应动态规划的工具和方法。将对所提出的学习算法进行严格的稳定性和鲁棒性分析以及收敛证明。该提案的一个根本新颖的方面是，将关注由微分方程描述的连续时间动力系统，而不是过去文献中使用的传统模型，例如离散时间系统和马尔可夫决策过程（MDP）。智力价值：这个跨学科项目旨在开发和验证鲁棒自适应动态规划作为人类感觉运动学习和控制的理论。为此，非常需要通过考虑更广泛的具有加性和乘性噪声以及强非线性的连续时间随机系统来开发超越现有文献的新结果。人类行为实验旨在测试和开发感觉运动控制的计算模型。更广泛的影响：尽管该项目针对的是人类运动问题，但预计该项目基于学习的控制理论的研究结果将有助于解决工程和计算神经科学（例如机器人康复）引起的其他问题。该提案还包括一项以学生监督、课程开发、知识传播以及与纽约大学神经科学中心合作为中心的教育活动计划。 PI希望在主要会议上组织研讨会和特邀会议，为学生和初级研究人员提供与自动控制和计算神经科学领域领先权威互动的机会。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。