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Abstract-Mobile robots that operate in a shared environment with humans need the ability to predict the movements of people to better plan their navigation actions. In this paper, we present a novel approach to predict the movements of pedestrians. Our method reasons about entire trajectories that arise from interactions between people in navigation tasks. It applies a maximum entropy learning method based on features that capture relevant aspects of the trajectories to determine the probability distribution that underlies human navigation behavior. Hence, our approach can be used by mobile robots to predict forthcoming interactions with pedestrians and thus react in a socially compliant way. In extensive experiments, we evaluate the capability and accuracy of our approach and demonstrate that our algorithm outperforms the popular social forces method, a state-of-the-art approach. Furthermore, we show how our algorithm can be used for autonomous robot navigation using a real robot.

Abstract-The problem of modeling the navigation behavior of multiple interacting agents arises in different areas including robotics, computer graphics, and behavioral science. In this paper, we present an approach to learn the composite navigation behavior of interacting agents from demonstrations. The decision process that ultimately leads to the observed continuous trajectories of the agents often also comprises discrete decisions, which partition the space of composite trajectories into homotopy classes. Therefore, our method uses a mixture probability distribution that consists of a discrete distribution over the homotopy classes and continuous distributions over the composite trajectories for each homotopy class. Our approach learns the model parameters of this distribution that match, in expectation, the observed behavior in terms of user-defined features. To compute the feature expectations over the highdimensional continuous distributions, we use Hamiltonian Markov chain Monte Carlo sampling. We exploit that the distributions are highly structured due to physical constraints and guide the sampling process to regions of high probability. We apply our approach to learning the behavior of pedestrians and demonstrate that it outperforms state-of-the-art methods.

Abstract-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 tele-operated 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.

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