Learning in Robotic Systems Using Biological Models

在这项研究中，猴子视觉-运动条件学习的神经网络和图式水平模型将被扩展到更广泛的伸展和抓取行为。这些模型将被用作机器人学习的基础，包括强化学习、分阶段学习、注意力集中和展示学习等范式。由此产生的神经网络策略将被用来使机器人学会使用视觉和触觉输入来抓取任意对象，构建积木组件，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接口。

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.