Improving robotic assembly performance through autonomous exploration

Abstract

Robots performing assembly tasks have the opportunity to acquire extensive experience, which could be used to improve performance. This paper reports on a highly encouraging result in which a force-controlled robot exploits a form of genetic algorithm to discover program improvements for mechanical assemblies. In tests on assembly of industrial components, the parameters discovered autonomously by the robot resulted in performance levels significantly better than we were able to tune manually. The result was also significantly better than human performance with these parts.