  The mechanisms of infant development are far from understood. Learning about
one's own body is likely a foundation for subsequent development. Here we look
specifically at the problem of how spontaneous touches to the body in early
infancy may give rise to first body models and bootstrap further development
such as reaching competence. Unlike visually elicited reaching, reaching to own
body requires connections of the tactile and motor space only, bypassing
vision. Still, the problems of high dimensionality and redundancy of the motor
system persist. In this work, we present an embodied computational model on a
simulated humanoid robot with artificial sensitive skin on large areas of its
body. The robot should autonomously develop the capacity to reach for every
tactile sensor on its body. To do this efficiently, we employ the computational
framework of intrinsic motivations and variants of goal babbling, as opposed to
motor babbling, that prove to make the exploration process faster and alleviate
the ill-posedness of learning inverse kinematics. Based on our results, we
discuss the next steps in relation to infant studies: what information will be
necessary to further ground this computational model in behavioral data.
