Hierarchical optimal control of complex dynamics - new algorithms and models of sensorimotor function

复杂动力学的层次优化控制——感觉运动功能的新算法和模型

• 批准号: 0524761
• 负责人: Emanuel Todorov
• 金额: $ 7.5万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-15 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0524761&HistoricalAwards=false
• 关键词: Hierarchical; optimal; control; complex; dynamics

Proposal Number: ECS-0524761Proposal Title: Hierarchical optimal control of complex dynamics - new algorithms and models of sensorimotor functionPI Name: Todorov, EmanuelPI Institution: University of California-San Diego Intellectual Merit: The PI plans to use and integrate more recent methods of adaptive dynamic programming or "reinforcement learning," and apply them to the modeling and control of biomechanical systems like human arm movement. Reinforcement learning methods have been applied before in biology and in the study of arm movement, but past studies have mainly relied on old, simple mathematical structures which do not scale well to high degrees of complexity in space and time. This project will make a unique effort to reach out, integrate and use more advanced methods. The effort to understand the mathematical, functional basis of effective decision and control in the brain is perhaps one of the most important, fundamental challenges before science in general.Broader Benefits: Cross-disciplinary communication between the most advanced areas of technology and the serious study of intelligence in the brain is still far less than it could be. If successful, this project could have a major impact on the unification of knowledge across disciplines. Aggressive education and dissemination are a natural part of the effort to heal the gap between disciplines related to these scientific goals.Better understanding of biomechanical issues may also have important benefits both in medicine and in robots.

提案编号：ECS-0524761提案标题：复杂动力学的分层最优控制-感觉运动功能的新算法和模型PI姓名：Todorov，Emanuel PI机构：加利福尼亚大学圣地亚哥分校智力优势：PI计划使用和整合自适应动态规划或“强化学习”的最新方法，并将其应用于生物力学系统（如人类手臂运动）的建模和控制。强化学习方法以前曾被应用于生物学和手臂运动的研究，但过去的研究主要依赖于旧的、简单的数学结构，这些结构在空间和时间上的复杂程度都不高。该项目将作出独特的努力，以接触，整合和使用更先进的方法。理解大脑有效决策和控制的数学和功能基础的努力可能是科学面临的最重要、最基本的挑战之一。更广泛的好处：最先进的技术领域和大脑智能的严肃研究之间的跨学科交流仍然远远不够。如果成功，该项目可能对跨学科知识的统一产生重大影响。积极的教育和传播是努力弥合与这些科学目标相关的学科之间的差距的自然组成部分。更好地理解生物力学问题也可能对医学和机器人都有重要的好处。