Neural-body coupling for emergent locomotion: A musculoskeletal quadruped robot with spinobulbar model

Abstract

To gain a synthetic understanding of how the body and nervous system co-create animal locomotion, we propose an investigation into a quadruped musculoskeletal robot with biologically realistic morphology and a nervous system. The muscle configuration and sensory feedback of our robot are compatible with the mono-and bi-articular muscles of a quadruped animal and with its muscle spindles and Golgi tendon organs. The nervous system is designed with a biologically plausible model of the spinobulbar system with no pre-defined gait patterns such that mutual entrainment is dynamically created by exploiting the physics of the body. In computer simulations, we found that designing the body and the nervous system of the robot with the characteristics of biological systems increases information regularities in sensorimotor flows by generating complex and coordinated motor patterns. Furthermore, we found similar results in robot experiments with the generation of various coordinated locomotion patterns created in a self-organized manner. Our results demonstrate that the dynamical interaction between the physics of the body with the neural dynamics can shape behavioral patterns for adaptive locomotion in an autonomous fashion. I. INTRODUCTIONThrough evolutionary processes, the animal morphology and nervous system have mutually adapted themselves in order to achieve efficient sensorimotor integration within the environment. As a result, various complex behaviors marked by efficiency in energy consumption as well as self-organization can emerge from dynamical interactions between the body, the neural system, and the environment. These skills are possible because the neural system exploits the physics of the body on the one hand, while on the other hand, the body dynamics structure the neural dynamics via sensory stimuli. This constitutes a fundamental property of embodied intelligence [1][2] [3].Recently, many researchers have followed this line of investigation to better understand the mechanisms underlying animal locomotor skills in order to apply them into robots [4] [5]. Particular attention has been focused on the central pattern generator (CPG) to replicate animal locomotion in biologically-inspired robots [6] [7]. For instance, the dog-like Tekken series [8] can accomplish stable locomotion pattern using sensory feedback whereas the insectlike AMOS-WD06 [9] can generate various complex behaviors by exploiting the chaotic properties of CPG models.

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