Learning novel objects for extended mobile manipulation

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

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)187-204
Number of pages18
JournalJournal of Intelligent and Robotic Systems: Theory and Applications
Issue number1-2
Publication statusPublished - 2012 Apr
Externally publishedYes


  • Mobile manipulation
  • Object learning
  • Object recognition
  • Out-of-vocabulary
  • RoboCup@Home

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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