Learning Sensorimotor Control of Balance and Locomotion

9812591SpongThe项目的目标是研究新的计算方法学习感觉运动控制在双足运动。 该项目的总体主题是工程学，心理学和运动学思想的交叉融合。PI将利用控制理论和人工智能技术来提高他们对人类运动的动力学和控制以及人类学习感觉运动控制的机制的理解，同时利用对人类运动的研究来帮助开发改进的学习和控制技术用于多自由度机械系统。 他们的计算方法的进一步应用可能包括更多的实用机器人和更有效的残疾人诊断和物理治疗方法，以及更好的平衡训练和老年人和平衡缺陷个体的福尔斯预防计划。他们组建了一个研究团队，具有机器人技术，非线性和混合控制理论，离散事件动态系统，机器学习和人工智能，计算机视觉，发展心理学和感知运动协调，从机器人和人类方面探索这些研究问题，并将这些不同学科的概念整合到多个学位的学习控制的连贯理论中。自由人类形态系统。他们将研究姿势控制，平衡，增益启动和步态转换的问题。 我们的目标将是开发反馈控制和学习机制的模型，首先涉及平衡控制，然后涉及运动。 这些模型将使用从人体运动研究中产生的数据开发，并将基于混合控制理论的最新技术，如监督控制和基于逻辑的开关控制。 人工智能的机器学习技术将用于从观测数据中学习开关控制策略。 该研究的新颖性在于：-基于非线性动力学和控制理论，混合控制理论和离散事件系统理论的最新概念，将人体运动研究与分析研究相结合。感知在平衡控制和行走控制中的重要作用。使用人工智能的机器学习方法来学习混合动力和开关控制策略。实验研究与分析研究相结合。 ***

9812591SpongThe goal of this project is to investigate new computational methods for learning sensorimotor control in bipedal locomotion. The overarching theme of the project is the integration of cross-fertilization of ideas from engineering, psychology, and kinesiology. The PI's will utilize techniques from control theory and artificial intelligence to improve their understanding of the dynamics and control of human movement and the mechanisms by which humans learn sensorimotor control, while at the same time utilize studies of human movement to aid the development of improved learning and control techniques for multi-degree-of-freedom mechanical systems. Further applications of their computational methods might include more dextrous and useful robots and more effective diagnostic and physical therapy approaches for disabled humans, as well as better balance training and falls prevention programs for elderly and individuals with balance deficits.They have assembled a team of researchers with expertise in robotics, nonlinear and hybrid control theory, discrete event dynamical systems, machine learning and artificial intelligence, computer vision, developmental psychology and perceptual-motor coordination to explore these research issues from both the robotic and the human side and to integrate concepts from these diverse disciplines into a coherent theory of learning control in multi-degree-of-freedom anthroopmorphic systems.They will study the problems of postural control, balance, gain initiation, and gait transition. The goal will be to develop models of the feedback control and learning mechanisms involved first in the control of balance and then in locomotion. The models will be developed using data generated from human movement studies and will be based on recent techniques of hybrid control theory, such as supervisory control and logic-based switching control. Machine learning techniques of artificial intelligence will be used to learn switching control strategies from observed data. The novelty of the research lies in:- The integration of human movement studies with analytical studies based on the most recent concepts of nonlinear dynamics and control theory, hybrid control theory, and discrete event systems theory.- The pre-eminent role attached to sensing for balance control and control of walking.- The use of machine learning methods of artificial intelligence to learn hybrid and switching control strategies.- The combination of experimental and analytical research. ***