Simulation-based Learning of the Peg-in-Hole Process Using Robot-Skills

Increasingly volatile markets challenge companies and demand flexible production systems that can be quickly adapted to new conditions. Machine Learning has proven to show significant potential in supporting the human operator during the time-consuming and complex task of robot programming by identifying relevant parameters of the underlying robot control program. We present a solution to learn these parameters for contact-rich, force-controlled assembly tasks from a simulation using hardware-independent robot skills. We show that successful learning and real-world execution are possible even under process deviation and tolerances utilizing the designed learning system. We present learning skill parameters as high-level robot control, evaluation and comparison of extensive simulations, and preliminary experiments on a physical robot test-bed. The developed solution approach is evaluated and discussed using the Peg-in-Hole process, a typical benchmark process in force-controlled assembly.