Optimising trajectories in simulations with deep reinforcement learning for industrial robots in automotive manufacturing

This paper outlines the concept of optimising trajectories for industrial robots by applying deep reinforcement learning in simulations. An application of high technical relevance is considered in a production line of an autmotive manufacturer (AUDI AG), where industrial manipulators apply sealant on a car body to prevent water intrusion and hence corrosion. A methodology is proposed that supports the human expert in the tedious task of programming the robot trajectories. A deep reinforcement learning agent generates trajectories in virtual instances where the use case is simulated. By making use of the automatically generated trajectories, the expert's task is reduced to minor changes instead of developing the trajectory from scratch. This paper describes an appropriate way to model the agent in the context of Markov decision processes and gives an overview of the employed technologies. The use case outlined in this paper is a proof of concept to demonstrate the applicability of reinforcement learning for industrial robotics.