Robot Skill Learning Through Intelligent Experimentation

Abstract

In robot skill learning the robot must obtain data for training by executing expensive practice trials and recording their results. The thesis is that the high cost of acquiring training data is the limiting factor in the performance of skill learners. Since the data is obtained from practice trials, it is important that the system make intelligent choices about what actions to attempt while practicing. In this dissertation we present several algorithms for intelligent experimentation in skill learning. In open-loop skills the execution goal is presented and the controller must then choose all the control signals for the duration of the task. Learning is a high-dimensional search problem. The system must associate a sequence of actions with each commandable goal. We propose an algorithm that selects practice actions most likely to improve performance by making use of information gained on previous trials. On the problem of learning to throw a ball using a robot with a flexible link, th...