基于小波变换的OFTM模拟学习搅拌食物

M. Falahi, Sima Sobhiyeh, A. Rezaie, S. Motamedi
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引用次数: 1

摘要

本文提出了一种基于模仿的机器人学习方法,使机器人只需一次演示即可学习新的轨迹。这种一次性学习方法是基于正交基函数和模板匹配(OFTM),这是我们小组之前提出的,并使用傅里叶基函数实现的。本文提出了一种将小波变换应用于OFTM方法的W-OFTM方法。在W-OFTM中,小波正交基函数被包含在原始运动字典中,同时还有一些已建立的模板。这种方法的一个主要优点是使机器人能够再现其工作空间中的所有轨迹。本研究在F-OFTM和W-OFTM方法中自动设置阈值参数,过滤掉不重要的系数,减少占用的内存空间,同时将增加的误差控制在一定的可接受值以下。在实验试验中,将该方法应用于厨师机器人,学习搅拌食物的任务。结果表明,与GMM-GMR方法相比,W-OFTM方法具有更高的精度和更小的延迟。此外,所提出的方法优于现有方法的优点随着轨迹中包含的样本数量的增加而增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wavelet based OFTM for learning stirring food by imitation
In this research a new robot learning method based on imitation is introduced which enables a robot to learn new trajectories by only one demonstration. This one-shot learning approach is based on Orthogonal basis Functions and Template Matching (OFTM) which was previously introduced by our group and implemented using the Fourier basis functions. In this paper the W-OFTM method is presented which employs the wavelet transform in the OFTM approach. In W-OFTM the wavelet orthogonal basis functions are included in the dictionary of primitive motions, alongside a few well-established templates. One of the major advantages of this approach is enabling the robot to reproduce all trajectories in its workspace. In this research, a thresholding parameter was automatically set in the F-OFTM and W-OFTM methods in order to filter out unimportant coefficients and reduce the occupied memory space while holding the increased error below a certain acceptable value. In the experimental trial, the proposed method was applied to a chef robot in order to learn the task of stirring food. Results indicate that in comparison to the GMM-GMR method, the W-OFTM method provides more accurate results with much less delay. Furthermore, the advantage of the proposed method over the state of the art method increases as the numbers of samples contained in a trajectory increases.
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