Integration of Knowledge-Based Systems and Neural Networks for Intellegent Sensorimotor Control

这项研究将开发一种混合技术， 灵巧机械手的控制。 最初的任务 在高级任务语言中指定的任务将被编码， 以神经网络的形式进行提炼。 第一阶段将确定 将该技术应用于难题的可行性 学习定位冗余的加载端点 操纵器受到多个目标和约束。 规则- 基于控制组件将训练，然后将控制转移到， 神经网络组件 网络性能将 通过强化学习和在线优化进行优化。 该技术结合了设计方便性和实现 将为机器人系统奠定基础， 获得在非结构化环境中执行复杂任务的技能 环境

This research will develop a hybrid technique for the intelligent control of dexterous robotic manipulators. Tasks initially specified in a high-level task language will be encoded and refined in a neural-nework form. Phase I will determine the feasibility of applying this technique to the difficult problem of learning to position the loaded endpoint of a redundant manipulator subject to multiple goals and constraints. Rule- based control components will train, then shift control to, neural network components. Network performance will then be refined through reinforcement learning and on-line optimization. This technique, combining design convenience with implementation efficiency, will lay the foundation for robotic systems able to acquire skills for performing complex tasks in unstructured environment.