  Decentralized coordination for multi-robot systems involves planning in
challenging, high-dimensional spaces. The planning problem is particularly
challenging in the presence of obstacles and different sources of uncertainty
such as inaccurate dynamic models and sensor noise. In this paper, we introduce
Stein Variational Belief Propagation (SVBP), a novel algorithm for performing
inference over nonparametric marginal distributions of nodes in a graph. We
apply SVBP to multi-robot coordination by modelling a robot swarm as a
graphical model and performing inference for each robot. We demonstrate our
algorithm on a simulated multi-robot perception task, and on a multi-robot
planning task within a Model-Predictive Control (MPC) framework, on both
simulated and real-world mobile robots. Our experiments show that SVBP
represents multi-modal distributions better than sampling-based or Gaussian
baselines, resulting in improved performance on perception and planning tasks.
Furthermore, we show that SVBP's ability to represent diverse trajectories for
decentralized multi-robot planning makes it less prone to deadlock scenarios
than leading baselines.
