Robotics: Science and Systems XVII

Dual Online Stein Variational Inference for Control and Dynamics

Lucas Barcelos, Alexander Lambert, Rafael Oliveira, Paulo Borges, Byron Boots, Fabio Ramos


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.



    AUTHOR    = {Lucas Barcelos AND Alexander Lambert AND Rafael Oliveira AND Paulo Borges AND Byron Boots AND Fabio Ramos}, 
    TITLE     = {{Dual Online Stein Variational Inference for Control and Dynamics}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2021}, 
    ADDRESS   = {Virtual}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2021.XVII.068}