Knowledge Commons of Institute of Automation,CAS
Skill Learning for Robotic Insertion Based on One-shot Demonstration and Reinforcement Learning | |
Ying Li1,2; De Xu1,2 | |
发表期刊 | International Journal of Automation and Computing |
ISSN | 1476-8186 |
2021 | |
卷号 | 18期号:3页码:457-467 |
摘要 | In this paper, an efficient skill learning framework is proposed for robotic insertion, based on one-shot demonstration and reinforcement learning. First, the robot action is composed of two parts: expert action and refinement action. A force Jacobian matrix is calibrated with only one demonstration, based on which stable and safe expert action can be generated. The deep deterministic policy gradients (DDPG) method is employed to learn the refinement action, which aims to improve the assembly efficiency. Second, an epis-ode-step exploration strategy is developed, which uses the expert action as a benchmark and adjusts the exploration intensity dynamically. A safety-efficiency reward function is designed for the compliant insertion. Third, to improve the adaptability with different components, a skill saving and selection mechanism is proposed. Several typical components are used to train the skill models. And the trained models and force Jacobian matrices are saved in a skill pool. Given a new component, the most appropriate model is selected from the skill pool according to the force Jacobian matrix and directly used to accomplish insertion tasks. Fourth, a simulation environment is established under the guidance of the force Jacobian matrix, which avoids tedious training process on real robotic systems. Simulation and experiments are conducted to validate the effectiveness of the proposed methods. |
关键词 | Force Jacobian matrix one-shot demonstration dynamic exploration strategy insertion skill learning reinforcement |
DOI | 10.1007/s11633-021-1290-3 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44294 |
专题 | 学术期刊_Machine Intelligence Research |
作者单位 | 1.Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China |
第一作者单位 | 精密感知与控制研究中心 |
推荐引用方式 GB/T 7714 | Ying Li,De Xu. Skill Learning for Robotic Insertion Based on One-shot Demonstration and Reinforcement Learning[J]. International Journal of Automation and Computing,2021,18(3):457-467. |
APA | Ying Li,&De Xu.(2021).Skill Learning for Robotic Insertion Based on One-shot Demonstration and Reinforcement Learning.International Journal of Automation and Computing,18(3),457-467. |
MLA | Ying Li,et al."Skill Learning for Robotic Insertion Based on One-shot Demonstration and Reinforcement Learning".International Journal of Automation and Computing 18.3(2021):457-467. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
IJAC-2020-10-274.pdf(1450KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
个性服务 |
推荐该条目 |
保存到收藏夹 |
查看访问统计 |
导出为Endnote文件 |
谷歌学术 |
谷歌学术中相似的文章 |
[Ying Li]的文章 |
[De Xu]的文章 |
百度学术 |
百度学术中相似的文章 |
[Ying Li]的文章 |
[De Xu]的文章 |
必应学术 |
必应学术中相似的文章 |
[Ying Li]的文章 |
[De Xu]的文章 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论