A Cerebellum-Inspired Prediction and Correction Model for Motion Control of a Musculoskeletal Robot

Abstract

It is an important issue that how to regulate the existing control models of musculoskeletal robots to improve the ability of motion learning and generalization. In this paper, based on the motion modulation function of the cerebellum, a cerebellum-inspired prediction and correction model is proposed to carry out feedforward regulation of the original controller. Firstly, drawing on the reservoir computing mechanism of the cerebellar granular layer, the cerebellum prediction model is established by using the echo state network. Incremental learning for the network is achieved using the replay method, which is able to process control signals with different distributions. The cerebellum prediction network can accurately predict the motion results of the robot under the action of the time-series control signals. Secondly, referring to the neural pathways of the cerebellum, the cerebellum correction model is constructed. The network learning rules are designed by drawing on the long-term potentiation and depression processes of cerebellar synaptic plasticity. The characteristics of the parameters in the network weight update equations are further analyzed. And the hyperparameter update rules of the correction network weights are proposed. To simulate the function of the cerebellum involved in the limb ballistic movement, the adaptive adjustment method for cerebellum correction duration is proposed. The cerebellum correction network can accurately modulate the original control signals by using the motion prediction results. In experiments, a musculoskeletal robot is used to verify the movement effects under the control of the cerebellum model. The results show that the cerebellum-inspired model can effectively improve the motion accuracy and learning efficiency of the musculoskeletal robot, and enhance the motion generalization ability and system robustness of the robot.