Robotics: Science and Systems XVII

Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems

Spencer M. Richards, Navid Azizan, Jean-Jacques Slotine, Marco Pavone


Real-time adaptation is imperative to the control of robots operating in complex; dynamic environments. Adaptive control laws can endow even nonlinear systems with good trajectory tracking performance; provided that any uncertain dynamics terms are linearly parameterizable with known nonlinear features. However; it is often difficult to specify such features a priori; such as for aerodynamic disturbances on rotorcraft or interaction forces between a manipulator arm and various objects. In this paper; we turn to data-driven modeling with neural networks to learn; offline from past data; an adaptive controller with an internal parametric model of these nonlinear features. Our key insight is that we can better prepare the controller for deployment with control-oriented meta-learning of features in closed-loop simulation; rather than regression-oriented meta-learning of features to fit input-output data. Specifically; we meta-learn the adaptive controller with closed-loop tracking simulation as the base-learner and the average tracking error as the meta-objective. With a nonlinear planar rotorcraft subject to wind; we demonstrate that our adaptive controller outperforms other controllers trained with regression-oriented meta-learning when deployed in closed-loop for trajectory tracking control.



    AUTHOR    = {Spencer M. Richards AND Navid Azizan AND Jean-Jacques Slotine AND Marco Pavone}, 
    TITLE     = {{Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2021}, 
    ADDRESS   = {Virtual}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2021.XVII.056}