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In this paper, we present the Role Playing Learning (RPL) scheme for a mobilerobot to navigate socially with its human companion in populated environments.Neural networks (NN) are constructed to parameterize a stochastic policy thatdirectly maps sensory data collected by the robot to its velocity outputs,while respecting a set of social norms. An efficient simulative learningenvironment is built with maps and pedestrians trajectories collected from anumber of real-world crowd data sets. In each learning iteration, a robotequipped with the NN policy is created virtually in the learning environment toplay itself as a companied pedestrian and navigate towards a goal in a sociallyconcomitant manner. Thus, we call this process Role Playing Learning, which isformulated under a reinforcement learning (RL) framework. The NN policy isoptimized end-to-end using Trust Region Policy Optimization (TRPO), withconsideration of the imperfectness of robot's sensor measurements. Simulativeand experimental results are provided to demonstrate the efficacy andsuperiority of our method.

Role Playing Learning for Socially Concomitant Mobile Robot Navigation