Active exploration for body model learning through self-touch on a humanoid robot with artificial skin

TL;DR

This paper presents an embodied computational model enabling a humanoid robot with artificial skin to autonomously learn to reach tactile sensors, using intrinsic motivation and goal babbling to improve exploration efficiency and address high-dimensional motor learning challenges.

Contribution

It introduces a novel approach combining intrinsic motivation and goal babbling for body model learning on a humanoid robot with artificial skin, advancing autonomous self-exploration methods.

Findings

Efficient autonomous reaching for tactile sensors achieved.

Intrinsic motivation accelerates exploration compared to motor babbling.

Framework addresses high-dimensional inverse kinematics learning.

Abstract

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.