Hidenori Kimura

Biomimetic Approach to Tacit Learning Based on Compound Control

The remarkable capability of living organisms to adapt to unknown environments is due to learning mechanisms that are totally different from the current artificial machine-learning paradigm. Computational media composed of identical elements that have simple activity rules play a major role in biological control, such as the activities of neurons in brains and the molecular interactions in intracellular control. As a result of integrations of the individual activities of the computational media, new behavioral patterns emerge to adapt to changing environments. We previously implemented this feature of biological controls in a form of machine learning and succeeded to realize bipedal walking without the robot model or trajectory planning. Despite the success of bipedal walking, it was a puzzle as to why the individual activities of the computational media could achieve the global behavior. In this paper, we answer this question by taking a statistical approach that connects the individual activities of computational media to global network behaviors. We show that the individual activities can generate optimized behaviors from a particular global viewpoint, i.e., autonomous rhythm generation and learning of balanced postures, without using global performance indices.

Emergence of bipedal walking through body/environment interactions

In biological regulatory systems, all computations result from spatial and temporal combination of simple and homogeneous computational media. This computational scheme realize the adaptability to unpredictable environmental changes, which is one of the most salient features of biological regulations. To investigate the learning process behind this computational scheme, we propose a learning method that embodies the features of biological systems, termed tacit learning. We have constructed a controller based on the notion of tacit learning and applied it to the control of the 36DOF humanoid robot to create the bipedal walking adapted to the environment. Experiments on walking showed a remarkably high adaptation capability of tacit learning in terms of gait generations, power consumption and robustness.

Voluntary and Reflex Muscle Synergies in Upper Limbs

It is known that the human muscles can be controlled both intentionally and by automatic responses. However how exactly the neural signals received by the muscles are produced is still unknown. One of the concepts created to answer this question are the muscle synergies. This concept however, doesn’t take into account whether the neural signals are of voluntary or involuntary origin. Most researchers apply this concept to analyze exclusively automatic responses or exclusively voluntary movements or both without distinguishing between them. We propose an extended synergy model that explicitly accounts for both voluntary and involuntary neural signals and try to verify it experimentally.We examine reaching movements with and without constraints that provoke automatic responses. The general goal of this research is the creation of a measure of recovery level in upper limb rehabilitation after brain stroke, as well as, a rehabilitation assisting device. We introduce the synergy stability index and with experiments we show that the synergy stability is lower for movements with disturbance that provoke an involuntary movement.

The Rule of the Dependency Level of the Sensory Synergy in Recruiting Muscle Synergy

The concept of sensory and muscle synergies have reemerged in neuroscience as a possible mechanism adopted by the central nervous system (CNS) to deal with the complexity of the sensorimotor signaling in advanced mammals. Many studies have proposed various strategies to extract and deal with such as synergies: 1) to gain a deep insight into the neuromuscular system in human, and 2) to reconstruct robust neuro-base rehabilitation techniques for stroke patients. This study is a part of a series of studies that aim to build an automated neurorehabilitation tracking system based on understanding the link between the sensory synergy (SS) and the muscle synergy (MS). More precisely, here we are exploring the underlying mechanisms of how the CNS relies on SS feedback to recruit the proper MS when executing a certain movement. This study is derived from experimental analysis of automatic posture responses to the lateral ground perturbations of seven healthy subjects with various balance abilities. Results reveal that the dependency level among the calculated joint-acceleration-based SS are likely to be a key point for tuning the muscle synergy to ensure the quality of the resulting movement.