Self-organizing skill synthesis

Abstract

Endowing robots with the ability of self-organizing learned skills enables them to learn a new skill from learned skills. This paper proposes a skill-synthesis framework to enable robots to self-organize learned skills for facilitating learning a new skill for accomplishing a new task. The proposed skill-synthesis framework differs from previous work in its capability of self-organizing learned skills into stimulus-response units (SRU), from which new skills can be quickly learned and synthesized. The proposed skill-synthesis framework can be realized by stages, which establish common SRUs between two similar skills and self-organize a new skill from these common SRUs and additional new SRUs with reinforcement learning. Computer simulations and experiments with a Pioneer 3-DX mobile robot were conducted to validate the self-organizing capability of the proposed skill-synthesis framework in identifying common SRUs between similar skills and learning new skills from learned skills.