This paper presents a semi-parametric algorithm for online learning of a robot inverse dynamics model. It combines the strength of the parametric and non-parametric modeling. The former exploits the rigid body dynamics equation, while the latter exploits a suitable kernel function. We provide an extensive comparison with other methods from the literature using real data from the iCub humanoid robot. In doing so we also compare two different techniques, namely cross validation and marginal likelihood optimization, for estimating the hyperparameters of the kernel function.
Online semi-parametric learning for inverse dynamics modeling / Romeres, D; Zorzi, M; Camoriano, R; Chiuso, A. - ELETTRONICO. - (2016), pp. 2945-2950. (Intervento presentato al convegno 2016 IEEE 55th Conference on Decision and Control (CDC) tenutosi a Las Vegas (USA) nel 12-14 December 2016) [10.1109/CDC.2016.7798708].
Online semi-parametric learning for inverse dynamics modeling
Camoriano R;
2016
Abstract
This paper presents a semi-parametric algorithm for online learning of a robot inverse dynamics model. It combines the strength of the parametric and non-parametric modeling. The former exploits the rigid body dynamics equation, while the latter exploits a suitable kernel function. We provide an extensive comparison with other methods from the literature using real data from the iCub humanoid robot. In doing so we also compare two different techniques, namely cross validation and marginal likelihood optimization, for estimating the hyperparameters of the kernel function.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2982146