Robot local navigation with learned social cost functions

Robot navigation in human environments is an active research area that poses serious challenges. Among them, human-awareness has gain lot of attention in the last years due to its important role in human safety and robot acceptance. The proposed robot navigation system extends state of the navigation schemes with some social skills in order to naturally integrate the robot motion in crowded areas. Learning has been proposed as a more principled way of estimating the insights of human social interactions. To do this, inverse reinforcement learning is used to derive social cost functions by observing persons walking through the streets. Our objective is to incorporate such costs into the robot navigation stack in order to “emulate” these human interactions. In order to alleviate the complexity, the system is focused on learning an adequate cost function to be applied at the local navigation level, thus providing direct low-level controls to the robot. The paper presents an analysis of the results in a robot navigating in challenging real scenarios, analyzing and comparing this approach with other algorithms.