Stein Variational Belief Propagation for Decentralized Multi-Robot Control

Jana Pavlasek, Joshua Mah, Odest Jenkins, Fabio Ramos

Keywords: probabilistic inference, belief propagation, multi-robot control

TL;DR: This paper performs decentralized multi-robot control as graphical inference using Stein Variational Belief Propagation.

Abstract: Decentralized control for multi-robot systems involves planning in complex, high-dimensional spaces. The planning problem is particularly challenging in the presence of potential collisions between robots and obstacles, and different sources of uncertainty such as inaccurate dynamic models and sensor noise. A multi-robot system can be represented as a graphical model, in which nodes represent individual robots and edges represent communication between robots. This representation enables the use of graphical inference algorithms for solving multi-robot control. In this short paper, we introduce Stein Variational Belief Propogation (SVBP), a novel algorithm for performing inference over the marginal distributions of nodes in a graph. We present simulation results which demonstrate that our method can represent complex, multi-modal distributions in localization and control tasks.