Global Dynamics: a new concept for design of dynamical behavior

The global dynamics, a novel concept for design of human/humanoid behavior is proposed. The principle of this concept is to exploit the body dynamics and apply control input only where it is necessary. Within the phase space of the body dynamics, there are many stable and unstable mani-folds coexist. Then if we analysed its structure and obtained a map in sufficient resolution, it may be possible to realise a motion by exploiting stable regions for reducing control input and unstable regions for switching between stable regions. Also, we expect an emergence of symbols within the dynamics, as the series of points where control input should be adopted. This feature realises higher level description and makes adaptation behavior easier. We are studying from two aspects, the motion capture experiment and dynamical simulation of simple elastic robot. The former supports that above assumption and the latter supports the exploiting the dynamical stability is useful

Emergent Spontaneous Movements Based on Embodiment: Toward a General Principle for Early Development

We investigate whether spontaneous movements, which initiate and guide early development in animals, can be accounted for by the properties underlying embodiment. We constructed computer and robotic models of several biological species with biologically plausible musculoskeletal bodies and nervous systems, and extracted the embodied and motor networks based on inter-muscle connectivities. In computer simulations and robot experiments, we found that the embodied and motor networks had similar global and local topologies, suggesting the key role of embodiment in generating spontaneous movements in animals

An algebraic theory to discriminate qualia in the brain

The mind-brain problem is to bridge relations between in higher mental events and in lower neural events. To address this, some mathematical models have been proposed to explain how the brain can represent the discriminative structure of qualia, but they remain unresolved due to a lack of validation methods. To understand the qualia discrimination mechanism, we need to ask how the brain autonomously develops such a mathematical structure using the constructive approach. Here we show that a brain model that learns to satisfy an algebraic independence between neural networks separates metric spaces corresponding to qualia types. We formulate the algebraic independence to link it to the other-qualia-type invariant transformation, a familiar formulation of the permanence of perception. The learning of algebraic independence proposed here explains downward causation, i.e. the macro-level relationship has the causal power over its components, because algebra is the macro-level relationship that is irreducible to a law of neurons, and a self-evaluation of algebra is used to control neurons. The downward causation is required to explain a causal role of mental events on neural events, suggesting that learning algebraic structure between neural networks can contribute to the further development of a mathematical theory of consciousness

Designing spontaneous behavioral switching via chaotic itinerancy

Chaotic itinerancy is a frequently observed phenomenon in high-dimensional and nonlinear dynamical systems, and it is characterized by the random transitions among multiple quasi-attractors. Several studies have revealed that chaotic itinerancy has been observed in brain activity, and it is considered to play a critical role in the spontaneous, stable behavior generation of animals. Thus, chaotic itinerancy is a topic of great interest, particularly for neurorobotics researchers who wish to understand and implement autonomous behavioral controls for agents. However, it is generally difficult to gain control over high-dimensional nonlinear dynamical systems. Hence, the implementation of chaotic itinerancy has mainly been accomplished heuristically. In this study, we propose a novel way of implementing chaotic itinerancy reproducibly and at will in a generic high-dimensional chaotic system. In particular, we demonstrate that our method enables us to easily design both the trajectories of quasi-attractors and the transition rules among them simply by adjusting the limited number of system parameters and by utilizing the intrinsic high-dimensional chaos. Finally, we quantitatively discuss the validity and scope of application through the results of several numerical experiments.Comment: 15 pages, 6 figures and 1 supplementary figure. Our supplementary videos are available in https://drive.google.com/drive/folders/10iB23OMHQfFIRejZstoXMJRpnpm3-3H5?usp=sharin

Emergence of Reaching using Predictive Learning as Sensorimotor Development in Complex Dynamics

The 11th International Symposium on Adaptive Motion of Animals and Machines. Kobe University, Japan. 2023-06-06/09. Adaptive Motion of Animals and Machines Organizing Committee.Poster Session P5

Emergent Spontaneous Movements Based on Embodiment: Toward a General Principle for Early Development

We investigate whether spontaneous movements, which initiate and guide early development in animals, can be accounted for by the properties underlying embodiment. We constructed computer and robotic models of several biological species with biologically plausible musculoskeletal bodies and nervous systems, and extracted the embodied and motor networks based on inter-muscle connectivities. In computer simulations and robot experiments, we found that the embodied and motor networks had similar global and local topologies, suggesting the key role of embodiment in generating spontaneous movements in animals

Self-organized acquisition of muscle synergy and behavior with whole body musculoskeletal infant model

The 11th International Symposium on Adaptive Motion of Animals and Machines. Kobe University, Japan. 2023-06-06/09. Adaptive Motion of Animals and Machines Organizing Committee.Poster Session P5

Homeostatic reinforcement learning explains foraging strategies

The 11th International Symposium on Adaptive Motion of Animals and Machines. Kobe University, Japan. 2023-06-06/09. Adaptive Motion of Animals and Machines Organizing Committee.Poster Session P6