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Mobile service robots are envisioned to operate in environments that are populated by humans and therefore ought to navigate in a socially compliant way. Since the desired behavior of the robots highly depends on the application, we need flexible means for teaching a robot a certain navigation policy. We present an approach that allows a mobile robot to learn how to navigate in the presence of humans while it is being teleoperated in its designated environment. Our method applies feature-based maximum entropy learning to derive a navigation policy from the interactions with the humans. The resulting policy maintains a probability distribution over the trajectories of all the agents that allows the robot to cooperatively avoid collisions with humans. In particular, our method reasons about multiple homotopy classes of the agents' trajectories, i. e., on which sides the agents pass each other. We implemented our approach on a real mobile robot and demonstrate that it is able to successfully navigate in an office environment in the presence of humans relying only on on-board sensors.
Kuderer et al. (Fri,) studied this question.