Open AccessManipulator Meta-Imitation Learning Algorithm with Memory Weight Integration
Author(s) -
Mingjun Yin,
Qingshan Zeng
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/569/5/052039
Subject(s) - forgetting , computer science , task (project management) , imitation , process (computing) , key (lock) , manipulator (device) , artificial intelligence , meta learning (computer science) , machine learning , robot , engineering , psychology , cognitive psychology , social psychology , computer security , systems engineering , operating system
Versatility is one of the key characteristics of general agent. In order to enable the manipulator to quickly and effectively acquire the ability to perform multiple tasks in an unknown environment, a large capacity model is essential. In this paper, the memory weight integration term adapted to meta-learning algorithm is proposed. By adjusting the plasticity of neurons, the manipulator can learn to learn more effectively in the process of learning multi-task and improve the forgetting problem of multi-task learning. Then, this paper combines the memory weight integration with meta-imitation learning, so that the manipulator can acquire new skills from a single demonstration task. Finally, a 7-DoF manipulator in PusherEnv experiment is used to explore the influence of different integration coefficients on the algorithm. The results show that the memory weight integration can effectively improve the success rate of tasks.