Role Playing Learning for Socially Concomitant Mobile Robot Navigation
This addresses the problem of enabling robots to navigate socially with humans in populated settings, though it appears incremental as it builds on existing reinforcement learning methods.
The paper tackles socially concomitant navigation for mobile robots with human companions in crowded environments by introducing Role Playing Learning (RPL), which uses neural networks and reinforcement learning to map sensory data to velocity outputs while adhering to social norms, achieving efficacy and superiority in simulations and experiments.
In this paper, we present the Role Playing Learning (RPL) scheme for a mobile robot to navigate socially with its human companion in populated environments. Neural networks (NN) are constructed to parameterize a stochastic policy that directly maps sensory data collected by the robot to its velocity outputs, while respecting a set of social norms. An efficient simulative learning environment is built with maps and pedestrians trajectories collected from a number of real-world crowd data sets. In each learning iteration, a robot equipped with the NN policy is created virtually in the learning environment to play itself as a companied pedestrian and navigate towards a goal in a socially concomitant manner. Thus, we call this process Role Playing Learning, which is formulated under a reinforcement learning (RL) framework. The NN policy is optimized end-to-end using Trust Region Policy Optimization (TRPO), with consideration of the imperfectness of robot's sensor measurements. Simulative and experimental results are provided to demonstrate the efficacy and superiority of our method.