Adaptive sensory-Motor Planning By Humans and machines

人类和机器的自适应感觉运动规划

• 批准号: 8716960
• 负责人: Stephen Grossberg
• 金额: $ 50.97万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-05-01 至 1991-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8716960&HistoricalAwards=false
• 关键词: Adaptive; sensory; Motor; Planning; Humans

The project will develop real-time neural network models for the adaptive control and planning of complex sensory-motor skills. Such models are equally important for understanding human movement planning and the design of flexible characterized circuits for generating synchronous movements of multi-joint motor systems, such as arms and speech articulators, at variable speeds. The current research will investigate the design of supplementary circuits that are capable of rapidly adapting to the unexpected loads and inertias that arise during novel movements, and of slowly learning to predict the correct movement parameters when these novel movements become familiar through practice. In addition, the project will attemp to analyze how elementary, or atomic, movements are organized into complex movement sequences, and how the controls for these sequences become automated through learning. Also, eye-hand coordination will be analyzed by showing how a representation of where the eye-head system is looking is associatively transformed into where the hand-arm system is commanded to move. Issues concerning the learned timing of actions and self-organization of a body-centered coordinated system will also be analyzed.

该项目将开发实时神经网络模型， 复杂传感器电机自适应控制与规划 skills. 这样的模型对于理解 人体运动规划与柔性化设计 产生多关节同步运动的电路 运动系统，例如手臂和言语发音器，处于不同的状态 速度. 目前的研究将调查的设计 能够快速适应的辅助电路 在小说创作过程中出现意外载荷和惯性 运动，并慢慢学习预测正确的运动 当这些新的运动变得熟悉， 实践 此外，该项目还将分析如何 基本或原子运动被组织成复杂的 运动序列，以及如何控制这些序列 通过学习实现自动化。 还有，手眼协调 将通过展示如何表示 眼-头系统的注视被关联地转化为 其中手臂系统被命令移动。 问题 关于行动和自我组织的学习时间 还将分析以身体为中心的协调系统。