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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.
|Title of host publication||Proceedings of the 3rd Conference on Learning for Dynamics and Control|
|Editors||Ali Jadbabaie, John Lygeros, George J. Pappas, Pablo A. Parrilo, Benjamin Recht, Claire J. Tomlin, Melanie N. Zeilinger|
|Number of pages||13|
|Publication status||Published - 7 Jun 2021|
|Event||3rd Annual Learning for Dynamics & Control Conference - Online|
Duration: 7 Jun 2021 → 8 Jun 2021
|Name||Proceedings of Machine Learning Research|
|Conference||3rd Annual Learning for Dynamics & Control Conference|
|Abbreviated title||L4DC 2021|
|Period||7/06/21 → 8/06/21|
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