Learning from Demonstration: Generalization via Task Segmentation

2017 International Conference on Artificial Intelligence Applications and Technologies

计算机科学

Ettehadi, N.^1 ; Manaffam, S.^1 ; Behal, A.^1

ECE Department, NanoScience Technology Center, University of Central Florida (UCF), Orlando

FL

32816, United States^1

关键词: Computationally efficient;    Experimental settings;    Learning from demonstration;    Motion segmentation;    Nominal trajectory;    Robotic platforms;    Task segmentation;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/261/1/012001/pdf
DOI  :  10.1088/1757-899X/261/1/012001

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

In this paper, a motion segmentation algorithm design is presented with the goal of segmenting a learned trajectory from demonstration such that each segment is locally maximally different from its neighbors. This segmentation is then exploited to appropriately scale (dilate/squeeze and/or rotate) a nominal trajectory learned from a few demonstrations on a fixed experimental setup such that it is applicable to different experimental settings without expanding the dataset and/or retraining the robot. The algorithm is computationally efficient in the sense that it allows facile transition between different environments. Experimental results using the Baxter robotic platform showcase the ability of the algorithm to accurately transfer a feeding task.