Skill Learning for Robotic Insertion Based on One-shot Demonstration and Reinforcement Learning

doi:10.1007/s11633-021-1290-3

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > International Journal of Automation and Computing

	Skill Learning for Robotic Insertion Based on One-shot Demonstration and Reinforcement Learning
	Ying Li 1,2; De Xu 1,2
刊名	International Journal of Automation and Computing
	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.
内容类型	期刊论文
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.