Motor Learning and Generalization Using Broad Learning Adaptive Neural Control
Motor Learning and Generalization Using Broad Learning Adaptive Neural Control
复制标题
使用广泛学习自适应神经控制进行运动学习和泛化
DOI：
10.1109/tie.2019.2950853
复制
发表时间：
2020-10-01
影响因子：
7.7
通讯作者：
Chen, C. L. Philip
中科院分区：
文献类型：
--
作者：
Huang, Haohui;Zhang, Tong;Chen, C. L. Philip
Human neural motor system has the intelligence to learn new skills, and then to generalize these skills naturally. But it is not easy for a robot to demonstrate such intelligent behaviors. Inspired by the neural motor behaviors, a framework of broad learning based novel adaptive neural control is proposed in this article, such that in the presence of dynamic disturbance, robots can learn a set of basic skills and then generalize these skills to the neighboring movements naturally as our human motor system. This is achieved by incorporating the deterministic learning with the broad learning system that can accumulate and reuse the learned knowledge. The broad learning enabled adaptive neural control has been rigorously established in theory and tested in both simulation and experimental studies. Simulation results and performance of the Baxter robot in the experiments have shown the effectiveness and superiority of the proposed method in comparison to the conventional adaptive neural control.
