Neural Comput - Chaotic exploration and learning of locomotion behaviors.

Tópicos

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Resumo

We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage.

Resumo Limpo

present general fulli dynam neural system exploit intrins chaotic dynam realtim goaldirect explor learn possibl locomot pattern articul robot arbitrari morpholog unknown environ control model network neural oscil initi coupl physic embodi goaldirect explor coordin motor pattern achiev chaotic search use adapt bifurc phase space indirect coupl neuralbodyenviron system contain multipl transient perman selforgan dynam candid locomot behavior adapt bifurc enabl system orbit wander various phasecoordin state use intrins chaotic dynam drive forc stabil one state match given goal criteria order improv sustain use transient pattern sensori homeostasi introduc result increas divers motor output thus achiev multiscal explor rhythmic pattern discov process memor sustain chang wire initi disconnect oscil use adapt synchron method result show novel neurorobot system abl creat learn multipl locomot behavior wide rang bodi configur physic environ can readapt realtim sustain damag

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