Skill decomposition by self-categorizing stimulus-response units

Endowing robots with the ability of skill learning enables them to be versatile and skillful in performing various tasks. This paper proposes a skill-decomposition framework, which differs from previous work in its capability of decomposing a skill by self-categorizing it into significant stimulus-response units (SRU). The proposed skill-decomposition framework can be realized by stages with a 5-layer neuro-fuzzy network with supervised learning, resolution control and reinforcement learning, to enable robots to identify a sufficient number of significant SRUs for accomplishing a given task. Computer simulations and experiments with a Pioneer DX-3 mobile robot were conducted to validate the self-categorization capability of the proposed skill-decomposition framework in learning and identifying significant SRUs from task examples.