Trabajo presentado al 9th IEEE-RAS celebrado en París del 7 al 10 de diciembre de 2009. ; This paper addresses the problem of hand-eye coordination and, more specifically, tool-eye recalibration of humanoid robots. Inspired by results from neuroscience, a novel method to learn the forward kinematics model as part of the body schema of humanoid robots is presented. By making extensive use of techniques borrowed from the field of computer-aided geometry, the proposed Kinematic Be ́zier Maps (KB-Maps) permit reducing this complex problem to a linearly-solvable, although high-dimensional, one. Therefore, in the absence of noise, an exact kinematic model is obtained. This leads to rapid learning which, unlike in other approaches, is combined with good extrapolation capabilities. These promising theoretical advantages have been validated through simulation, and the applicability of the method to real hardware has been demonstrated through experiments on the humanoid robot ARMAR-IIIa. ; This work was supported by projects: 'Perception, action & cognition through learning of object-action complexes.' (4915), 'Analysis and motion planning of complex robotic systems' (4802), 'Grup de recerca consolidat - Grup de Robòtica' (4810). The work described in this paper was partially conducted within the EU Cognitive Systems projects GRASP (FP7-215821) and PACO-PLUS (FP6-027657) funded by the European Commission. The authors acknowledge support from the Generalitat de Catalunya under the consolidated Robotics group, and from the Spanish Ministry of Science and Education, under the project DPI2007-60858 ; Peer Reviewed
Rapid learning of humanoid body schemas with kinematic Bezier maps
2009-01-01
Conference paper
Electronic Resource
English
DDC: | 629 |
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