Practical numerical algorithms for chaotic systems
Practical numerical algorithms for chaotic systems
Relevance of dynamic clustering to biological networks
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
Metalearning and neuromodulation
Neural Networks - Computational models of neuromodulation
Contribution of stretch reflexes to locomotor control: a modeling study
Biological Cybernetics
Biological Cybernetics - Special Issue: Dynamic Principles
Biological Cybernetics - Special Issue: Dynamic Principles
How the Body Shapes the Way We Think: A New View of Intelligence (Bradford Books)
How the Body Shapes the Way We Think: A New View of Intelligence (Bradford Books)
Learning to Move in Modular Robots using Central Pattern Generators and Online Optimization
International Journal of Robotics Research
Creating and modulating rhythms by controlling the physics of the body
Autonomous Robots
What do the basal ganglia do? A modeling perspective
Biological Cybernetics
Modeling basal ganglia for understanding parkinsonian reaching movements
Neural Computation
Reinforcement learning by chaotic exploration generator in target capturing task
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Neuronal assembly dynamics in supervised and unsupervised learning scenarios
Neural Computation
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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.