Structural descriptions in human-assisted robot visual learning

ABSTRACT

The paper presents an approach to using structural descriptions, obtained through a human-robot tutoring dialogue, as labels for the visual object models a robot learns. The paper shows how structural descriptions enable relating models for different aspects of one and the same object, and how being able to relate descriptions for visual models and discourse referents enables incremental updating of model descriptions through dialogue (either robot- or human initiated). The approach has been implemented in an integrated architecture for human-assisted robot visual learning.

REFERENCES

• 1: N. Asher and A. Lascarides. Logics of Conversation. Cambridge University Press, 2003.
• 2: Jason Baldridge , Geert-Jan M. Kruijff, Coupling CCG and hybrid logic dependency semantics, Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, July 07-12, 2002, Philadelphia, Pennsylvania [doi>10.3115/1073083.1073137]
• 3: David G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, v.60 n.2, p.91-110, November 2004 [doi>10.1023/B:VISI.0000029664.99615.94]
• 4: D. K. Roy. Semiotic schemas: A framework for grounding language in the action and perception. Artificial Intelligence, to appear.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.10 Vision and Scene Understanding

General Terms:
Algorithms

Keywords:
cognitive vision and learning, natural language dialogue