Skill Learning for Robotic Insertion Based on One-shot Demonstration and Reinforcement Learning
文献类型：期刊论文
| 作者 | Ying Li1,2 ; De Xu1,2
|
| 刊名 | International Journal of Automation and Computing
 |
| 出版日期 | 2021 |
| 卷号 | 18期号:3页码:457-467 |
| 关键词 | Force Jacobian matrix one-shot demonstration dynamic exploration strategy insertion skill learning reinforcement |
| ISSN号 | 1476-8186 |
| DOI | 10.1007/s11633-021-1290-3 |
| 英文摘要 | 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/44294]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 2.Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
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。