  Ensuring safe navigation in human-populated environments is crucial for
autonomous mobile robots. Although recent advances in machine learning offer
promising methods to predict human trajectories in crowded areas, it remains
unclear how one can safely incorporate these learned models into a control loop
due to the uncertain nature of human motion, which can make predictions of
these models imprecise. In this work, we address this challenge and introduce a
distributionally robust chance-constrained model predictive control (DRCC-MPC)
which: (i) adopts a probability of collision as a pre-specified, interpretable
risk metric, and (ii) offers robustness against discrepancies between actual
human trajectories and their predictions. We consider the risk of collision in
the form of a chance constraint, providing an interpretable measure of robot
safety. To enable real-time evaluation of chance constraints, we consider
conservative approximations of chance constraints in the form of
distributionally robust Conditional Value at Risk constraints. The resulting
formulation offers computational efficiency as well as robustness with respect
to out-of-distribution human motion. With the parallelization of a
sampling-based optimization technique, our method operates in real-time,
demonstrating successful and safe navigation in a number of case studies with
real-world pedestrian data.
