Learning Novel Objects for Extended Mobile Manipulation

We propose a method for learning novel objects from audio visual input. The proposed method is based on two techniques: out-of-vocabulary (OOV) word segmentation and foreground object detection in complex environments. A voice conversion technique is also involved in the proposed method so that the...

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Bibliographic Details
Published inJournal of intelligent & robotic systems Vol. 66; no. 1-2; pp. 187 - 204
Main Authors Nakamura, Tomoaki, Sugiura, Komei, Nagai, Takayuki, Iwahashi, Naoto, Toda, Tomoki, Okada, Hiroyuki, Omori, Takashi
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.04.2012
Springer Nature B.V
Subjects
Artificial Intelligence
Control
Conversion
Electrical Engineering
Engineering
Interactive
Learning
Mechanical Engineering
Mechatronics
Robotics
Robots
Tasks
Visual
Object learning
Out-of-vocabulary
RoboCup@Home
Object recognition
Mobile manipulation
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Summary:We propose a method for learning novel objects from audio visual input. The proposed method is based on two techniques: out-of-vocabulary (OOV) word segmentation and foreground object detection in complex environments. A voice conversion technique is also involved in the proposed method so that the robot can pronounce the acquired OOV word intelligibly. We also implemented a robotic system that carries out interactive mobile manipulation tasks, which we call “extended mobile manipulation”, using the proposed method. In order to evaluate the robot as a whole, we conducted a task “Supermarket” adopted from the RoboCup@Home league as a standard task for real-world applications. The results reveal that our integrated system works well in real-world applications.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0921-0296
1573-0409
DOI:10.1007/s10846-011-9605-1