Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Recognizing Texture and Hardness by Touch

Johnsson, Magnus LU and Balkenius, Christian LU orcid (2008) International Conference on Intelligent Robots and Systems (IROS) 2008 p.482-487
Abstract
We have experimented with different neural network based architectures for bio-inspired self-organizing texture and hardness perception systems. To this end we have developed a microphone based texture sensor and a hardness
sensor that measures the compression of the material at a constant pressure.We have implemented and successfully tested both monomodal systems for texture and hardness perception and multimodal systems that merge texture and hardness data into one representation. All systems were trained and tested
with multiple samples gained from the exploration of a set of 4soft and 4 hard objects of different materials. The monomodal texture system was good at mapping individual objects in a sensible way, the hardness... (More)
We have experimented with different neural network based architectures for bio-inspired self-organizing texture and hardness perception systems. To this end we have developed a microphone based texture sensor and a hardness
sensor that measures the compression of the material at a constant pressure.We have implemented and successfully tested both monomodal systems for texture and hardness perception and multimodal systems that merge texture and hardness data into one representation. All systems were trained and tested
with multiple samples gained from the exploration of a set of 4soft and 4 hard objects of different materials. The monomodal texture system was good at mapping individual objects in a sensible way, the hardness systems was good at mapping individual objects and in addition dividing the objects into categories of hard and soft objects. The multimodal system was successful in merging the two modalities into a representation that performed at least as good as the best recognizer of individual objects, i.e. the texture system, and at the same time
categorizing the objects into hard and soft. (Less)
Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : Acropolis convention center, Nice, France. September 22-26, 2008. Proceedings - Acropolis convention center, Nice, France. September 22-26, 2008. Proceedings
editor
Chatila, Raja ; Kelly, Alonzo and Merlet, Jean-Pierre
pages
6 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
International Conference on Intelligent Robots and Systems (IROS) 2008
conference location
Nice, France
conference dates
2008-09-22 - 2008-09-26
external identifiers
  • scopus:69549090016
ISBN
9781424420575
9781424420582
DOI
10.1109/IROS.2008.4650676
project
Ikaros: An infrastructure for system level modelling of the brain
language
English
LU publication?
yes
id
f05cd381-9b13-4c23-9478-45a8b1274683 (old id 1288041)
date added to LUP
2016-04-04 14:32:28
date last changed
2025-01-07 10:25:34
@inproceedings{f05cd381-9b13-4c23-9478-45a8b1274683,
  abstract     = {{We have experimented with different neural network based architectures for bio-inspired self-organizing texture and hardness perception systems. To this end we have developed a microphone based texture sensor and a hardness<br/>sensor that measures the compression of the material at a constant pressure.We have implemented and successfully tested both monomodal systems for texture and hardness perception and multimodal systems that merge texture and hardness data into one representation. All systems were trained and tested<br/>with multiple samples gained from the exploration of a set of 4soft and 4 hard objects of different materials. The monomodal texture system was good at mapping individual objects in a sensible way, the hardness systems was good at mapping individual objects and in addition dividing the objects into categories of hard and soft objects. The multimodal system was successful in merging the two modalities into a representation that performed at least as good as the best recognizer of individual objects, i.e. the texture system, and at the same time<br/>categorizing the objects into hard and soft.}},
  author       = {{Johnsson, Magnus and Balkenius, Christian}},
  booktitle    = {{2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : Acropolis convention center, Nice, France. September 22-26, 2008. Proceedings}},
  editor       = {{Chatila, Raja and Kelly, Alonzo and Merlet, Jean-Pierre}},
  isbn         = {{9781424420575}},
  language     = {{eng}},
  pages        = {{482--487}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Recognizing Texture and Hardness by Touch}},
  url          = {{http://dx.doi.org/10.1109/IROS.2008.4650676}},
  doi          = {{10.1109/IROS.2008.4650676}},
  year         = {{2008}},
}