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We present an algorithm enabling a humanoid robot to visually learn its body schema, knowing only the number of degrees of freedom in each limb. By body schema we mean the body shape, the motor positions and orientations and thus the kinematic function. The learning is performed by visually observing its end-effectors when moving them and/or by touching its own body segments and is proved to converge to an adequate solution. With simulations involving a body schema of more than 20 degrees of freedom, results show that the system is scalable to a high number of degrees of freedom. Real robot experiments confirm the practicality of our approach. Our results illustrate how subjective space representation can develop as a result of sensory-motor contingencies.


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Last update: 25/08/06