Predicting Human Navigation Behavior via Inverse Reinforcement Learning

We present an approach that allows a mobile robot to learn the behavior of pedestrians from observed trajectories. Our method maintains probability distributions over composite trajectories of all the pedestrians and represents these distributions as a mixture model. The upper level of this model repre- sents a discrete distribution over classes of trajectories that are equivalent according to a set of features, such as passing on the left or passing on the right side. The lower level comprises continuous probability distributions over trajectories for each of these classes and captures physical features of the trajectories, such as velocities and accelerations. For each level, our method learns maximum entropy distributions that match the feature values of the observations. To estimate the expected feature values with respect to the high-dimensional probability distributions over the composite trajectories, our approach applies Hamiltoni- an Markov Chain Monte Carlo sampling. The extensive experimental evaluation suggests that our method models human navigation behavior more accurately than state-of-the-art techniques.

Authors

• Henrik Kretzschmar
• Markus Kuderer
• Wolfram Burgard University of Freiburg

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