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

这项研究将为灵巧机器人的智能控制开发一种混合技术。最初用高级任务语言指定的任务将以神经网络的形式进行编码和细化。第一阶段将确定将这一技术应用于学习定位受多个目标和约束的冗余机械手的加载端点这一难题的可行性。基于规则的控制组件将训练，然后将控制转移到神经网络组件。然后通过强化学习和在线优化来优化网络性能。这种技术结合了设计的方便性和实现的效率，将为机器人系统能够在非结构化环境中获得执行复杂任务的技能奠定基础。

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.