Dual Online Stein Variational Inference for Control and Dynamics

Model predictive control (MPC) schemes have a proven track record for delivering aggressive and robust performance in many challenging control tasks; coping with nonlinear system dynamics; constraints; and observational noise. Despite their success; these methods often rely on simple control distributions; which can limit their performance in highly uncertain and complex environments. MPC frameworks must be able to accommodate changing distributions over system parameters; based on the most recent measurements. In this paper; we devise an implicit variational inference algorithm able to estimate distributions over model parameters and control inputs on-the-fly. The method incorporates Stein Variational gradient descent to approximate the target distributions as a collection of particles; and performs updates based on a Bayesian formulation. This enables the approximation of complex multi-modal posterior distributions; typically occurring in challenging and realistic robot navigation tasks. We demonstrate our approach on both simulated and real-world experiments requiring real-time execution in the face of dynamically changing environments.