Is it my body? Body extraction from uninterpreted sensory data based on the invariance of multiple sensory attributes

Finding the body in uninterpreted sensory data is one of the fundamental competences to construct the body representation that influences on adaptabilities of the robot to the changes in the environment and the robot body. The invariance of sensations in self-observation seems a promising key information to find the body. However, since each sensory attribute can be invariant only in the observation of a part of the body, the robot should complementarily utilize the invariance of the multiple sensory attributes. In this paper, we propose a method of body-nonbody discrimination by complementarily utilizing multiple sensory attributes based on a conjecture about the distribution of the variance of sensations for each observing posture, where it can be approximated by a mixture of two Gaussian distributions, which are for observing the body and the nonbody, respectively. By estimating the distribution, the robot can automatically find a discrimination hyperplane to judge whether it observes its body in the current observing posture. Simple experiments show the validity of the proposed method.