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Autonomous online learning of velocity kinematics on the iCub: A comparative study

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4 Author(s)
Droniou, A. ; Inst. des Syst. Intelligents et de Robot., Univ. Pierre et Marie Curie, Paris, France ; Ivaldi, S. ; Padois, V. ; Sigaud, O.

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.

Published in:

Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on

Date of Conference:

7-12 Oct. 2012

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