%0 Journal Article
%T Iterative learning neural network control for robot learning from demonstration
迭代学习神经网络控制在机器人示教学习中的应用(英文)
%A JIANG Ping
%A LI Zi-yu
%A CHEN Yang-quan
%A
蒋 平
%A 李自育
%A 陈阳泉
%J 控制理论与应用
%D 2004
%I
%X Learning from demonstration is an efficient way for transferring movement skill from a human teacher to a robot. Using a camera as a recorder of the demonstrated movement, a learning strategy is required to acquire knowledge about the \{nonlinearity\} and uncertainty of a robot-camera system through repetitive practice. The purpose of this paper is to design a neural network controller for vision-based movement imitation by repetitive tracking and to keep the maximum training deviation from a demonstrated trajectory in a permitted region. A distributed neural network structure along a demonstrated trajectory is proposed. The local \{networks\} for a segment of the trajectory are invariant or repetitive over repeated training and are independent of the other segments. As a result, a demonstrated trajectory can be decomposed into short segments and the training of the local neural \{networks\} can be done segment-wise progressively from the starting segment to the ending one. The accurate tracking of the whole demonstrated trajectory is thus accomplished in a step-by-step or segment-by-segment manner. It is used for trajectory imitation by demonstration with an unknown robot-camera model and shows that it is effective in ensuring uniform boundedness and efficient training.
%K iterative learning control
%K neural network control
%K visual servoing
%K imitation learning
迭代学习控制
%K 神经网络控制
%K 视觉伺服
%K 模仿学习
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=6AA54EC820ECE564&yid=D0E58B75BFD8E51C&vid=659D3B06EBF534A7&iid=38B194292C032A66&sid=C66DE7562B0326E2&eid=30F3EEEA29E34EE7&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=3&reference_num=8