Autonomous Online Learning of Velocity Kinematics on the iCub: a Comparative Study - CNRS - Centre national de la recherche scientifique Accéder directement au contenu
Communication Dans Un Congrès Année : 2012

Autonomous Online Learning of Velocity Kinematics on the iCub: a Comparative Study

Résumé

In the last years, several regression algorithms have been proposed to learn accurate mechanical models of robots. Comparisons are proposed at the conceptual level or through the use of recorded databases, but they deliver limited conclusions with respect to the real performance of these algorithms in their true context of use, i.e. online learning on the real robot interacting with its environment, within a feedback control loop. In this paper, we provide an empirical study of three state-of-the-art regression methods through online learning on the iCub robot holding a tool. We show that they can effectively learn a visuo-motor kinematic model for a simple visual servoing task in a very limited time (few minutes), without making any a priori hypothesis on the geometry of the robot and its tool. Furthermore, we can draw from the results some stronger conclusions about the comparison of the algorithms than previous studies based on databases.
Fichier principal
Vignette du fichier
2012_iros.pdf (601.26 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00719964 , version 1 (23-07-2012)

Identifiants

  • HAL Id : hal-00719964 , version 1

Citer

Alain Droniou, Serena Ivaldi, Vincent Padois, Olivier Sigaud. Autonomous Online Learning of Velocity Kinematics on the iCub: a Comparative Study. IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct 2012, Vilamoura, Portugal. To appear. ⟨hal-00719964⟩
126 Consultations
209 Téléchargements

Partager

Gmail Facebook X LinkedIn More