Move Beyond Trajectories: Distribution Space Coupling for Crowd Navigation

Cooperatively avoiding collision is a critical functionality for robots navigating in dense human crowds; failure of which could lead to either overaggressive or overcautious behavior. A necessary condition for cooperative collision avoidance is to couple the prediction of the agents' trajectories with the planning of the robot's trajectory. However; it is unclear that trajectory based cooperative collision avoidance captures the correct agent attributes. In this work we migrate from trajectory based coupling to a formalism that couples agent preference distributions. In particular; we show that preference distributions (probability density functions representing agents' intentions) can capture higher order statistics of agent behaviors; such as willingness to cooperate. Thus; coupling in distribution space exploits more information about inter-agent cooperation than coupling in trajectory space. We thus introduce a general objective for coupled prediction and planning in distribution space; and propose an iterative best response optimization method based on variational analysis with guaranteed sufficient decrease. Based on this analysis; we develop a sampling-based motion planning framework called DistNav that runs in real time on a laptop CPU. We evaluate our approach on challenging scenarios from both real world datasets and simulation environments; and benchmark against a wide variety of model based and machine learning based approaches. The safety and efficiency statistics of our approach outperform all other models. Finally; we find that DistNav is competitive with human safety and efficiency performance.