This paper presents a data-driven nonlinear disturbance observer to reduce the reality gap caused by the imperfect simulation of the real-world physics. The main focus is on increasing robustness of the closed-loop control without changing the RL algorithm or simulation model to account for the uncertainty of the real world. For this purpose, a DNN representing inverse dynamics of the deterministic source-domain environment is learned by the simulation data. The proposed approach offers a systematic way to transfer the policies trained in simulation into the real world without decreasing sample efficiency of the RL agent in contrast to domain randomization or min-max robust RL methods.     «

This paper presents a data-driven nonlinear disturbance observer to reduce the reality gap caused by the imperfect simulation of the real-world physics. The main focus is on increasing robustness of the closed-loop control without changing the RL algorithm or simulation model to account for the uncertainty of the real world. For this purpose, a DNN representing inverse dynamics of the deterministic source-domain environment is learned by the simulation data. The proposed approach offers a system...     »