Learning Novel Objects for Extended Mobile Manipulation

Authors:
Tomoaki Nakamura;Komei Sugiura;Takayuki Nagai;Naoto Iwahashi;Tomoki Toda;Hiroyuki Okada;Takashi Omori
Affiliations:
The University of Electro-Communications, Tokyo, Japan;National Institute of Information and Communications Technology, Kyoto, Japan;The University of Electro-Communications, Tokyo, Japan;National Institute of Information and Communications Technology, Kyoto, Japan;Nara Institute of Science and Technology, Nara, Japan;Tamagawa University, Tokyo, Japan;Tamagawa University, Tokyo, Japan
Venue:
Journal of Intelligent and Robotic Systems
Year:
2012

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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.