Self-tuning of robot program primitives

Abstract

Strategies used and parameter selection problems encountered in developing robot programs are addressed by describing an approach to self-tuning of robot program parameters. In this approach, the robot program incorporates control primitives with adjustable parameters and an associated cost function. A hybrid gradient-based and direct-search algorithm uses experimentally measured performance data to adjust the parameters to seek optimal performance and track system variations. Alternative control strategies which have first been optimized with the same cost function are then assessed in terms of their optimized behavior. It is demonstrated that the optimal control strategy for a particular task is a function not only of task geometry, but also of the desired performance.