首页|A Bio-Inspired Integration Model of Basal Ganglia and Cerebellum for Motion Learning of a Musculoskeletal Robot

A Bio-Inspired Integration Model of Basal Ganglia and Cerebellum for Motion Learning of a Musculoskeletal Robot

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It is a significant research direction for highly complex musculoskeletal robots that how to develop the ability of motion learning and generalization.The cooperations of multiple brain regions are crucial to improving motion performance.Inspired by the neural mechanisms of structures,functions,and interconnections of basal ganglia and cerebellum,a biologically inspired integration model for motor learning of musculoskeletal robots is proposed.Based on the neural characteristics of the basal ganglia,the basal ganglia actor network,which mainly simulates the dorsal striatum,outputs motion commands,and the basal ganglia critic network,which simulates the ventral striatum,estimates action-state values.Their network parameters are updated using the soft actor-critic method.Based on the sensorimotor prediction mechanism of the cerebellum,the cerebellum network evaluates the state feature extraction quality of the basal ganglia actor network and then updates the weights of its feature layer.This learning method is proven to converge to the optimal policy.Furthermore,drawing on the mechanism of dopaminergic dynamic regulation in the basal ganglia,the adaptive adjustment of target entropy and the dopaminergic experience replay are proposed to further improve the integration model,which contributes to the exploration-exploitation trade-off of motor learning.The bio-inspired integration model is validated on a musculoskeletal system.Experimental results indicate that this model can effectively control the musculoskeletal robot to accomplish the motion task from random starting locations to random target positions with high precision and robustness.

Basal ganglia and cerebellumbio-inspired integration modelmotion learningmuscu-loskeletal robotreinforcement learning

ZHANG Jinhan、CHEN Jiahao、ZHONG Shanlin、QIAO Hong

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State Key Laboratory of Multimodal Artificial Intelligence Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China

School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049,China

Major Project of Science and Technology Innovation 2030 Brain Science and Brain-Inspired Intelligence国家自然科学基金国家自然科学基金国家自然科学基金重点项目国家自然科学基金重点项目国家自然科学基金重点项目

2021ZD02004086220343962203443T2293720T2293723T2293724

2024

10.1007/s11424-024-3414-7

系统科学与复杂性学报(英文版)
中国科学院系统科学研究所

系统科学与复杂性学报(英文版)

EI
影响因子：0.181
ISSN：1009-6124
年,卷(期)：2024.37(1)
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