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

Abstract:

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.

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Bibtex:

  
@INPROCEEDINGS{Barcelos-RSS-21, 
    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} 
}