The unsupervised learning of assembly using discrete event control

Abstract

A new task-level adaptive controller which learns online is presented for the discrete event control of robotic tasks. Using a discrete event model of the assembly task, velocity constraints are derived from which desired velocity commands are obtained. Due to modelling errors and uncertainties, the velocity commands may result in a suboptimal, unwanted contacts between the workpiece and the environment. A task-level adaptive control scheme based on the occurrence of discrete events is used to change the model parameters from which the velocity commands are determined. Using Lyapunov stability theory, the adaptive control scheme is proved to converge to the actual model parameters. The method is applied to a starter motor assembly in an industrial setting. The results are excellent. The robot adapts its motion online to successfully assemble the starter motor. Moreover, the robot improves its motion with each subsequent trial. This online learning ability demonstrates the power of the discrete event approach to robotic manipulation.