Skill-based Programming of Force-controlled Assembly Tasks using Deep Reinforcement Learning
The selection of parameters for force-controlled assembly tasks remains a time consuming process. Skill-based approaches offer a guidance in parameter space, but still require expert knowledge to tune the parameters accordingly. Recently, Deep Reinforcement Learning algorithms have been used more frequently to learn the parameters of complex assembly tasks. For the industrial application of Reinforcement Learning, it is crucial to increase the training efficiency. Most approaches are based on engineered force-controllers or contact-models and focus on one specific robot. In this paper, we propose a framework, using state-of-the-art model-free algorithms and manipulation skills to learn force and position parameters in a simulation environment.