Layered neural-based locomotion for biped robot movement with carrying dynamic payload

This paper proposes layered neural based locomotion with a hierarchical learning process. Central pattern generation (CPG) in a higher layer generates an analog signal to the lower layer which is Motor Neurons Pools. In a lower layer, motor neuron generates the angular velocity of the joint. Central pattern generation is built based on neural oscillator model in the spinal cord. It responds to the pattern of locomotion model. Then, the inner state of a motor neuron is developed based on muscle activity in the human musculoskeletal model. Furthermore, the motor neurons pools (MNs) are integrated with sensory neurons (SNs) that send the internal feedback of the robot. There are two steps of the learning process: 1) optimizing the structure of CPG for generating appropriate behavior without considering the feedback information 2) optimizing the integration between MNs and SNs for generating adaptive behavior toward internal disturbance. The proposed model has been implemented in a simulated humanoid robot with carrying different weight of payloads. The robot behavior changes for stabilizing the robot posture.