Incremental Embodied Chaotic Exploration of Self-Organized Motor Behaviors with Proprioceptor Adaptation - Shim, Yoonsik; Husbands, Phil (Frontiers in Robotics and AI, 2015)
This paper presents a general and fully dynamic embodied neural system, which incrementally explores and learns motor behaviours through an integrated combination of chaotic search and reflex learning. The former uses adaptive bifurcation to exploit the intrinsic chaotic dynamics arising from neuro-body-environment interactions, while the latter is based around proprioceptor adaptation. The architecture developed here allows realtime goal-directed exploration and learning of the possible motor patterns (e.g. for locomotion) of embodied systems of arbitrary morphology. Examples of its successful application to a simple biomechanical model and a simulated 3D swimming robot are given. The tractability of the biomechanical systems allows detailed analysis of the overall dynamics of the search process. This analysis points out strong parallels with evolutionary search.