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.
