ARDL - A Library for Adaptive Robotic Dynamics Learning

Joshua Smith, Michael Mistry

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Dynamics learning and adaptive control algorithms have received a lack of support from robot dynamics libraries over the years. Only a few existing libraries like Pinocchio implement the standard regressor for basic model learning. In this work we introduce an open-source dynamics library specifically designed to provide support for dynamics learning and online adaptive control algorithms. Alongside established kinematics and dynamics computations, our new dynamics library provides computation for the standard, the Slotine-Li and the filtered regressor matrices found in adaptive control algorithms. We demonstrate the library through several existing adaptive control algorithms, alongside a new online simultaneous Semi-Parametric model using a Radial Basis Function Neural Network augmented with a newly derived consistency transform.
Original languageEnglish
Title of host publicationProceedings of the 3rd Conference on Learning for Dynamics and Control
EditorsAli Jadbabaie, John Lygeros, George J. Pappas, Pablo A. Parrilo, Benjamin Recht, Claire J. Tomlin, Melanie N. Zeilinger
PublisherPMLR
Pages754-766
Number of pages13
Publication statusPublished - 7 Jun 2021
Event3rd Annual Learning for Dynamics & Control Conference - Online
Duration: 7 Jun 20218 Jun 2021
https://l4dc.ethz.ch/

Publication series

NameProceedings of Machine Learning Research
PublisherPMLR
Volume144
ISSN (Print)2640-3498

Conference

Conference3rd Annual Learning for Dynamics & Control Conference
Abbreviated titleL4DC 2021
Period7/06/218/06/21
Internet address

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