Learning Novel Objects for Extended Mobile Manipulation View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-04

AUTHORS

Tomoaki Nakamura, Komei Sugiura, Takayuki Nagai, Naoto Iwahashi, Tomoki Toda, Hiroyuki Okada, Takashi Omori

ABSTRACT

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. More... »

PAGES

187-204

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10846-011-9605-1

DOI

http://dx.doi.org/10.1007/s10846-011-9605-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1010331641


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