Explanation-based learning of generalized robot assembly plans

Abstract

This thesis describes an experiment involving the application of a recently developed machine learning technique, explanation-based learning, to the robot retraining problem. Explanation-based learning permits a system to acquire generalized problem-solving knowledge on the basis of a single observed problem-solving example. The resulting computer program, called ARMS for Acquiring Robotic Manufacturing Schemata, serves as a medium for discussing issues related to this particular type of learning. This work clarifies and extends the corpus of knowledge so that explanation-based learning can be successfully applied to real-world problems. From a machine learning perspective, ARMS is one of the more ambitious working explanation-based learning implementations to date. Unlike many other vehicles for machine-learning research, the ARMS system operates in a non-trivial domain conveying the flavor of a real robot assembly application. From a robotics perspective, ARMS represents an important first step towards a learning-apprentice system for manufacturing. It posits a theoretically more satisfactory solution to the robot retraining problem, and offers an eventual alternative to the limitations of robot programming.