An application of reinforcement learning algorithms to industrial multi-robot stations for cooperative handling operation

This paper presents a novel approach to operate industrial robots as used for manufacturing lines within a cooperative robot station. The proposed framework consists of the application of especially to the cooperative robot handling problem adjusted Reinforcement Learning (RL) algorithms. Such RL-algorithms deal with sequential decision making processes in a trial-and-error learning interaction with the environment, to finally gain an optimal team-working behavior among the robots. In particular application results to a real team-working robot station underline the effectiveness of the novel RL approach.