Manifold Learning Approach toward Image Feature-based State Space Construction

Yuichi Kobayashi, Ryosuke Matsui, Toru Kaneko

2013

Abstract

This paper presents a bottom-up approach to building internal representation of an autonomous robot under a stand point that the robot create its state space for planning and generating actions only by itself. For this purpose, image-feature-based state space construction method is proposed using LLE (locally linear embedding). The visual feature is extracted from sample images by SIFT (scale invariant feature transform). SOM (Self Organizing Map) is introduced to find appropriate labels of image features throughout images with different configurations of robot. The vector of visual feature points mapped to low dimensional space express relation between the robot and its environment. The proposed method was evaluated by experiment with a humanoid robot collision classification.

Paper Citation

in Harvard Style

Kobayashi Y., Matsui R. and Kaneko T. (2013). Manifold Learning Approach toward Image Feature-based State Space Construction . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 529-534. DOI: 10.5220/0004630305290534

in Bibtex Style

@conference{ncta13,
author={Yuichi Kobayashi and Ryosuke Matsui and Toru Kaneko},
title={Manifold Learning Approach toward Image Feature-based State Space Construction},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013)},
year={2013},
pages={529-534},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004630305290534},
isbn={978-989-8565-77-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013)
TI - Manifold Learning Approach toward Image Feature-based State Space Construction
SN - 978-989-8565-77-8
AU - Kobayashi Y.
AU - Matsui R.
AU - Kaneko T.
PY - 2013
SP - 529
EP - 534
DO - 10.5220/0004630305290534