Grey-Box Learning of Adaptive Manipulation Primitives for Robotic Assembly

Autonomous learning of robotic manipulation tasks is a promising approach to reduce manual engineering effort and increase flexibility in the future of industrial manu- facturing. Although a lot of research has been done especially robotic assembly tasks requiring contact-rich compliant interac- tion remain a challenge for learning-based methods, since large amounts of interaction data are required. Incorporation of prior knowledge has long been seen as a possibility to make learning- based approaches tractable. The question is how can we enable process experts to encode their prior knowledge in grey-box models so that it can be used for learning robotic manipulation tasks? For that reason we propose a new grey-box learning approach, “Adaptive Manipulation Primitives” (AMP), intro- duced in this paper. AMPs combine compliant manipulation task specifications based on Manipulation Primitives Nets with Policy Gradient Reinforcement Learning. Our framework is evaluated in a real-world robotic assembly task. It is shown that learning to assemble industrial connector modules is possible with comparatively few real-world trials.