Effects of Domain Randomization on Simulation-to-Reality Transfer of Reinforcement Learning Policies for Industrial Robots

A requirement for a significant amount of training as well as the exploration of potentially expensive or safety-critical states limits the applicability of reinforcement learning for real-world robotics. One potential solution is given by pretraining models in simulations before transferring them to the real world. In this chapter, we investigate the concept of domain randomization to train robust agents in simulation to control an industrial robot. We examine the effects of different degrees of randomization with respect to the transferability to the real world. In addition, we use attention maps to gain insights into the agents’ decision-making processes. We find that attention maps enable a qualitative assessment for the data-efficiency of a pretrained agent when transferred to the real-world setup.