Control-oriented meta-learning
Name
2204.06716v1.pdf
Description
Accepted version
Size
1.57 MB
Format
Adobe PDF
Checksum (MD5)
d06e5d77bdf27050b7ef5aa177c77643
Author(s)
Richards, Spencer M.
Azizan, Navid
Slotine, Jean-Jacques
Pavone, Marco
Date Issued
June 7, 2023
Journal
The International Journal of Robotics Research
Publisher
SAGE Publications
Citation
Richards SM, Azizan N, Slotine J-J, Pavone M. Control-oriented meta-learning. The International Journal of Robotics Research. 2023;42(10):777-797.
Version
Author's final manuscript
Abstract
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 both fully-actuated and underactuated 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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1177/02783649231165085
