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Learning Robot Navigation Behaviors by Demonstration Using a RRT Planner Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Data Augmentation for Object Recognition of Dynamic Learning Robot

Authors : Jiunn Yuan Chan, Shuzhi Sam Ge, Chen Wang, Mingming Li

Published in: Social Robotics

Publisher: Springer International Publishing

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Abstract

The training of deep learning networks for robot object recognition requires a large database of training images for satisfactory performance. The term “dynamic learning” in this paper refers to the ability of a robot to learn new features under offline conditions by observing its surrounding objects. A training framework for robots to achieve object recognition with satisfactory performance under offline training conditions is proposed. A coarse but fast method of object saliency detection is developed to facilitate raw image collection. Additionally, a training scheme referred to as a Dynamic Artificial Database (DAD) is proposed to tackle the problem of overfitting when training neural networks without validation data.

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previous chapter Collaborative Visual Object Tracking via Hierarchical Structure
next chapter Rotational Coordinate Transformation for Visual-Inertial Sensor Fusion
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Metadata
Title
Data Augmentation for Object Recognition of Dynamic Learning Robot
Authors
Jiunn Yuan Chan
Shuzhi Sam Ge
Chen Wang
Mingming Li
Copyright Year
2016
Book
Social Robotics

Print ISBN: 978-3-319-47436-6

Electronic ISBN: 978-3-319-47437-3

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-47437-3_41

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