Data-Driven Policy Transfer With Imprecise Perception Simulation

This letter presents a complete pipeline for learning continuous motion control policies for a mobile robot when only a nondifferentiable physics simulator of robot–terrain interactions is available. The multimodal state estimation of the robot is also complex and difficult to simulate, so we simultaneously learn a generative model which refines simulator outputs. We propose a  coarse-to-fine learning paradigm, where the coarse motion planning is alternated with guided learning and policy transfer to the real robot. The policy is jointly optimized with the generative model. We evaluate the method on a real-world platform.