ARDL - A Library for Adaptive Robotic Dynamics Learning

Joshua Smith, Michael Mistry
Proceedings of the 3rd Conference on Learning for Dynamics and Control, PMLR 144:754-766, 2021.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v144-smith21a, title = {{ARDL} - A Library for Adaptive Robotic Dynamics Learning}, author = {Smith, Joshua and Mistry, Michael}, booktitle = {Proceedings of the 3rd Conference on Learning for Dynamics and Control}, pages = {754--766}, year = {2021}, editor = {Jadbabaie, Ali and Lygeros, John and Pappas, George J. and A. Parrilo, Pablo and Recht, Benjamin and Tomlin, Claire J. and Zeilinger, Melanie N.}, volume = {144}, series = {Proceedings of Machine Learning Research}, month = {07 -- 08 June}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v144/smith21a/smith21a.pdf}, url = {https://proceedings.mlr.press/v144/smith21a.html}, 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.} }
Endnote
%0 Conference Paper %T ARDL - A Library for Adaptive Robotic Dynamics Learning %A Joshua Smith %A Michael Mistry %B Proceedings of the 3rd Conference on Learning for Dynamics and Control %C Proceedings of Machine Learning Research %D 2021 %E Ali Jadbabaie %E John Lygeros %E George J. Pappas %E Pablo A. Parrilo %E Benjamin Recht %E Claire J. Tomlin %E Melanie N. Zeilinger %F pmlr-v144-smith21a %I PMLR %P 754--766 %U https://proceedings.mlr.press/v144/smith21a.html %V 144 %X 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.
APA
Smith, J. & Mistry, M.. (2021). ARDL - A Library for Adaptive Robotic Dynamics Learning. Proceedings of the 3rd Conference on Learning for Dynamics and Control, in Proceedings of Machine Learning Research 144:754-766 Available from https://proceedings.mlr.press/v144/smith21a.html.

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