Learning Novel Objects for Extended Mobile Manipulation

Tomoaki Nakamura

doi:10.1007/s10846-011-9605-1

Learning Novel Objects for Extended Mobile Manipulation

2011-07-23

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AI

DOI：10.1007/s10846-011-9605-1

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Abstract

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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.

Keywords:

Mobile manipulation

Object learning

Object recognition

Out-of-vocabulary

RoboCup@Home

Learning Novel Objects for Extended Mobile Manipulation

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