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Recognizing Texture and Hardness by Touch

Johnsson, Magnus LU and Balkenius, Christian LU (2008) IEEE/RSJ International Conference on Intelligent Robots and Systems In 2008 IEEE/RSJ INTERNATIONAL CONFERENCE ON ROBOTS AND INTELLIGENT SYSTEMS, VOLS 1-3, CONFERENCE PROCEEDINGS 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 4 soft 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... (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 4 soft 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)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
2008 IEEE/RSJ INTERNATIONAL CONFERENCE ON ROBOTS AND INTELLIGENT SYSTEMS, VOLS 1-3, CONFERENCE PROCEEDINGS
pages
482 - 487
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
IEEE/RSJ International Conference on Intelligent Robots and Systems
external identifiers
  • WOS:000259998200076
  • Scopus:69549090016
language
English
LU publication?
yes
id
feca6ca3-7a3f-4752-891b-2745b8c1fb42 (old id 1285080)
date added to LUP
2009-02-10 14:10:08
date last changed
2017-01-01 08:06:56
@inproceedings{feca6ca3-7a3f-4752-891b-2745b8c1fb42,
  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 4 soft 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.},
  author       = {Johnsson, Magnus and Balkenius, Christian},
  booktitle    = {2008 IEEE/RSJ INTERNATIONAL CONFERENCE ON ROBOTS AND INTELLIGENT SYSTEMS, VOLS 1-3, CONFERENCE PROCEEDINGS},
  language     = {eng},
  pages        = {482--487},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Recognizing Texture and Hardness by Touch},
  year         = {2008},
}