Imitation Learning Based on Visuo-Somatic Mapping

Abstract

Imitation learning is a powerful approach to humanoid behavior generation, however, the most existing methods assume the availability of the information on the internal state of a demonstrator such as joint angles, while humans usually cannot directly access to imitate the observed behavior. This paper presents a method of imitation learning based on visuosomatic mapping from observing the demonstrator’s posture to reminding the self posture via mapping from the self motion observation to the self posture for both motion understanding and generation. First, various kinds of posture data of the observer are mapped onto posture space by self organizing mapping (hereafter, SOM), and the trajectories in the posture space are mapped onto a motion segment space by SOM again for data reduction. Second, optical flows caused by the demonstrator’s motions or the self motions are mapped onto a flow segment space where parameterized flow data are connected with the corresponding motion segments in the motion segment space. The connection with the self motion is straightforward, and is easily acquired by Hebbian Learning. Then, the connection with the demonstrator’s motion is automatic based on the learned connection. Finally, the visuo-somatic mapping is completed when the posture space (the observer: self) and image space (the demonstrator: other) are connected, which means observing the demonstrator’s posture associcates the self posture. Experimental results with human motion data are shown and the discussion is given with future issues.