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A neural network with central pattern generators entrained by sensory feedback controls walking of a bipedal model

A neuromechanical simulation of a planar, bipedal walking robot has been developed. It is constructed as a simplified, planar musculoskeletal model of the biomechanics of the human lower body. The controller consists of a dynamic neural network with central pattern generators (CPGs) entrained by force and movement sensory feedback to generate appropriate muscle forces for walking. The CPG model is a two-level architecture, which consists of separate rhythm generator and pattern formation networks. The biped model walks stably in the sagittal plane without inertial sensors or a centralized posture controller or a ‘baby walker’ to help overcome gravity. Its gait is similar to humans’ and it walks at speeds from 0.850 m s−1 up to 1.289 m s−1 with leg length of 0.84 m. The model walks over small unknown steps (6% of leg length) and up and down 5° slopes without any additional higher level control actions.