Learning in Robotic Systems Using Biological Models

使用生物模型在机器人系统中学习

• 批准号: 9221582
• 负责人: Michael Arbib
• 金额: $ 55.7万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-06-15 至 1998-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9221582&HistoricalAwards=false
• 关键词: Learning; Robotic; Systems; Using; Biological

In this research, neural network and schemalevel models of visual- motor conditional learning in monkeys will be extended to cover a wider range of reaching and grasping behaviors. These models will then be used as a basis for robot learning, including such paradigms as reinforcement learning, staged learning, focus of attention, and learning by showing. The resulting neural net strategies will then be used to enable a robot to learn to use visual and tactile input to grasp arbitrary objects, to construct assemblies of blocks, and to coordinate the motion of two arms. Existing tools for neural network simulation will be interfaced with software for robot sensing and control.

在这项研究中，猴子视觉运动条件学习的神经网络和图式模型将被扩展到涵盖更广泛的触及和抓握行为。 这些模型将用作机器人学习的基础，包括强化学习、分阶段学习、注意力集中和通过展示学习等范式。 由此产生的神经网络策略将用于使机器人能够学习使用视觉和触觉输入来抓取任意物体、构建块组件以及协调两个手臂的运动。 现有的神经网络模拟工具将与机器人传感和控制软件相连接。