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Real-world learning control for autonomous exploration of a biomimetic robotic shark | |
Yan Shuaizheng1![]() ![]() ![]() ![]() ![]() ![]() | |
发表期刊 | IEEE Transactions on Industrial Electronics
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2022-05 | |
卷号 | 70期号:4页码:3966-3974 |
摘要 | With the development of learning-based autonomous underwater exploration method of robotic fish, how to improve data quality and sampling efficiency, so as to achieve better control performance, becomes a challenging research subject. To address this issue, in this article, a feasible real-world deep reinforcement learning framework for autonomous underwater exploration of robotic fish is proposed, which ably avoids model discrepancy generated in virtual training. The designed framework consists of three phases: 1) teaching initialization; 2) regular update of reinforcement learning; and 3) phased consolidation training. Especially, reasonable teaching initialization improves the data sampling efficiency and stabilizes the real-world early training. The consolidation training ensures the reproducibility of good controllers by interim imitation learning in the middle training phase. Extensive underwater experiments on a novel self-developed biomimetic robotic shark show that the proposed real-world learning method significantly improves the safety and efficiency of autonomous exploration based on local sensor information, providing a promising solution for exploring in uncharted wild waters. |
收录类别 | SCI |
七大方向——子方向分类 | 智能机器人 |
国重实验室规划方向分类 | 水下仿生机器人 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51842 |
专题 | 复杂系统认知与决策实验室 |
通讯作者 | Wu Zhengxing |
作者单位 | 1.Laboratory of Cognitive and Decision Intelligence for Complex System, Institute of Automation, Chinese Academy of Sciences 2.State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, BIC-ESAT, College of Engineering, Peking University |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Yan Shuaizheng,Wu Zhengxing,Wang Jian,et al. Real-world learning control for autonomous exploration of a biomimetic robotic shark[J]. IEEE Transactions on Industrial Electronics,2022,70(4):3966-3974. |
APA | Yan Shuaizheng,Wu Zhengxing,Wang Jian,Huang Yupei,Tan Min,&Yu Junzhi.(2022).Real-world learning control for autonomous exploration of a biomimetic robotic shark.IEEE Transactions on Industrial Electronics,70(4),3966-3974. |
MLA | Yan Shuaizheng,et al."Real-world learning control for autonomous exploration of a biomimetic robotic shark".IEEE Transactions on Industrial Electronics 70.4(2022):3966-3974. |
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Real-World_Learning_(5981KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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