Hopping height control of an active suspension type leg module based on reinforcement learning and a neural network

The aim of our study is to have a hopping module to control the height of hopping in an environment where the control parameters are unknown. This will lead to the development of a system for building dynamic walking robots. Assuming that a hopping module can be controlled by a spring and a DC motor, we placed a built-in learning system in the module that consists of reinforcement learning (RL) for identification and layered neural networks (NN) for generalization. By using this learning system, we simulated autonomous adjustment control in order to obtain the optimum DC motor angular velocity, which enables the module to hop to an arbitrary height. As a result, we can design a regulator that has the advantage of both RL and NN, and have laid the foundation for further developments to apply the algorithms of learning to practical walking robots.