Primitives Generation Policy Learning without Catastrophic Forgetting for Robotic Manipulation
Fangzhou Xiong1,2; Zhiyong Liu1,2,3; Kaizhu Huang4; Xu Yang1,2; Amir Hussain5
2019
Conference NameIEEE International Conference on Data Mining
Conference DateNovember 8-11, 2019
Conference PlaceChina National Convention Center (CNCC), Beijing
Abstract
Catastrophic forgetting is a tough challenge when agent attempts to address different tasks sequentially without storing previous information, which gradually hinders the development of continual learning. Except for image classification tasks in continual learning, however, there are little reviews related to robotic manipulation. In this paper, we present a novel hierarchical architecture called Primitives Generation Policy Learning to enable continual learning. More specifically, a generative method by Variational Autoencoder is employed to generate state primitives from task space, then separate policy learning component is designed to learn torque control commands for different tasks sequentially. Furthermore, different task policies could be identified automatically by comparing reconstruction loss in the autoencoder. Experiment on robotic manipulation task shows that the proposed method exhibits substantially improved performance over some other continual learning methods.
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/38523
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Corresponding AuthorZhiyong Liu
Affiliation1.State Key Lab of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Science, China
2.School of Computer and Control, University of Chinese Academy of Sciences (UCAS), China
3.CAS Centre for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, China
4.Department of EEE, Xi'an Jiaotong-Liverpool University, China
5.School of Computing, Edinburgh Napier University, U.K.
Recommended Citation
GB/T 7714
Fangzhou Xiong,Zhiyong Liu,Kaizhu Huang,et al. Primitives Generation Policy Learning without Catastrophic Forgetting for Robotic Manipulation[C],2019.
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