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Spined Torso Renders Advanced Mobility for Quadrupedal Locomotion

Jichao Wang, Jinyu Cheng, Jiangtao Hu, Wei Gao, Shiwu Zhang

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Abstract

Animals possessing spinal columns often exhibit exceptional agility for highly dynamic locomotion. The spine grants the trunk with increased degrees of freedom, thereby endowing diverse postures. This paper presents the development of a robot STRAY for quadrupedal locomotion, featuring a four-degree-of-freedom spine design. Using trajectory based reinforcement learning techniques, STRAY is able to trot and bound dynamically using its spine. Simulation results reveal the positive roles of spinal movement, such as twisting, extension, retraction and rotation, in helping STRAY realize efficient locomotion. Preliminary results from experiments demonstrate that STRAY can achieve a trotting gait of approximately 0.6 m/s and a bounding gait of 0.7 m/s, with desired velocities of 0.8 m/s and 1.0 m/s, respectively. The results also indicate that reinforcement learning is a feasible way to investigate how the spine should be used in dynamic quadrupedal locomotion and achieve more possibilities in the future.

Index terms

Biologically-Inspired Robots Model Learning for Control Reinforcement Learning