Single leg operational space control of quadruped robot based on reinforcement learning

Due to the strongly environmental adaptation, quadruped robot becomes a research hot spot. The single leg control lays foundation for the control of quadruped robot. The operational space control which is used in the quadruped robot single leg control algorithms usually strongly depends on the accuracy of the model. This paper applies RBF (radial basis function) neural network adaptive control on the single leg, and a kind of control parameters adjustment method which is based on reinforcement learning is proposed. The result shows the algorithm effectively improves the control accuracy and convergence speed under the high-dynamic condition.