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 manufacturing. Although a lot of research has been done especially robotic assembly tasks requiring contact-rich compliant interaction 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), introduced 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.