A Computational Framework of Goal Directed Voluntary Motion Generation and Control Loop in Humanoid Robots

Abstract

In this paper, it is aimed to construct a computational framework related to bio-inspired motion generation and control systems for humanoid robots. To acquire natural motion patterns in humanoid robots, behaviors observed from biological motor systems in humans and other mammals should be analyzed in detail. Computational mechanisms are mainly placed on the biophysical plausible neural structures embodied in different dynamics. The main components of the system are composed of the limbic system, neocortex, cerebellum, brainstem, and spinal cord modules. Internal dynamics of these modules include a nonlinear estimator (e.g. chaotic attractor), memory formation, learning (neural plasticity) procedure. While the proposed novel neurocognitive framework is performing goal-directed voluntary motion generation and control tasks, also it estimates the amount of motion errors and computes motion correction signals. By this study, some motion-based central nervous system lesions (e.g. epilepsy, Parkinson, etc.) can be computationally modeled so that impairments of motor control commands are detected. Thus motion disorders can be reconstructed not only in humanoid robots but also in humans via some locomotion equipment.