A Neural Primitive model with Sensorimotor Coordination for Dynamic Quadruped Locomotion with Malfunction Compensation

In the field of quadruped locomotion, dynamic locomotion behavior, and rich integration with sensory feedback represents a significant development. In this paper, we present an efficient neural model, which includes CPG and its sensorimotor coordination, and demonstrate its implementation in a quadruped robot to show how efficient integration of motor and sensory feedback can generate dynamic behavior and how sensorimotor coordination reconstructs the sensory network for leg malfunction compensation. Additionally, we delineate a network optimization strategy and suggest sensorimotor coordination as a strategy for controlling speed and regulating internal and external adaptation. The rhythm generation representing the leg injury was inactive, stimulating the sensorimotor system to reconstruct the network between CPG and feet force afferent without any commanding parameter. The performances of the simulated and real, cat-like robot on both flat and rough terrains and the leg malfunction tests demonstrated the effectiveness of the proposed model, indicating that a smooth gait-pattern transition could be generated during sudden leg malfunction.