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Sense of Touch in Robots

Johnsson, Magnus LU (2009) In Lund University Cognitive Studies 141.
Abstract
This thesis discusses a number of robot touch perception systems. All these systems are self-organizing and model different aspects of human touch perception. The thesis starts out with introductions to the anatomy and the physiology of the human hand. It proceeds with an overview of the human touch perception from both a neurophysiological and a behavioural perspective. Then related robotic research is reviewed and artificial neural networks are introduced.



The thesis proceeds by describing the robot platforms developed for the research. These platforms consist of three robot hands, the LUCS Haptic Hands I-III, together with texture and hardness sensors. The thesis discusses both touch sensor based and proprioception... (More)
This thesis discusses a number of robot touch perception systems. All these systems are self-organizing and model different aspects of human touch perception. The thesis starts out with introductions to the anatomy and the physiology of the human hand. It proceeds with an overview of the human touch perception from both a neurophysiological and a behavioural perspective. Then related robotic research is reviewed and artificial neural networks are introduced.



The thesis proceeds by describing the robot platforms developed for the research. These platforms consist of three robot hands, the LUCS Haptic Hands I-III, together with texture and hardness sensors. The thesis discusses both touch sensor based and proprioception based systems. Both kinds of systems successfully learned to separate objects of different size and shape as well as individual objects. Several texture and hardness sensor based systems are also discussed. Some of the systems are bimodal, i.e. they merge texture/hardness or touch/proprioception for improved performance. Two novel variants of the Self-Organizing Map that have been developed and used in some of the systems are discussed.



All systems have been tested and evaluated with different test objects. The systems based on the LUCS Haptic Hand I successfully learned to separate objects of different size. The systems based on the LUCS Haptic Hand II successfully learned to separate objects of different shapes and to discriminate individual objects. The systems based on the anthropomorphic robot hand, the LUCS Haptic Hand III, were all based on proprioceptive information. These systems successfully learned to separate objects of different shapes and sizes. They were also able to discriminate individual objects. The texture and hardness based systems were able to discriminate individual objects and to categorize them as hard or soft. The hardness and texture sensors were also used in a system that successfully developed associated representations of these two submodalities. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • Dr Pipe, Anthony, Department of School of Electrical and Computer Engineering, University of the West of England, Bristol
organization
publishing date
type
Thesis
publication status
published
subject
keywords
touch perception, neural network, robot hand, brain model, cognitive model, robot model, tensor network, associative self-organizing map, SOM, proprioception
in
Lund University Cognitive Studies
volume
141
pages
197 pages
defense location
Sal 104, Kungshuset, Lundagård, Lund
defense date
2009-01-19 10:00:00
ISSN
1101-8453
ISBN
978-91-977380-3-3
project
Ikaros: An infrastructure for system level modelling of the brain
language
English
LU publication?
yes
id
8ea87106-8dae-4506-a2ab-602404a0f783 (old id 1272108)
date added to LUP
2016-04-04 09:33:14
date last changed
2019-05-21 20:46:39
@phdthesis{8ea87106-8dae-4506-a2ab-602404a0f783,
  abstract     = {{This thesis discusses a number of robot touch perception systems. All these systems are self-organizing and model different aspects of human touch perception. The thesis starts out with introductions to the anatomy and the physiology of the human hand. It proceeds with an overview of the human touch perception from both a neurophysiological and a behavioural perspective. Then related robotic research is reviewed and artificial neural networks are introduced. <br/><br>
<br/><br>
The thesis proceeds by describing the robot platforms developed for the research. These platforms consist of three robot hands, the LUCS Haptic Hands I-III, together with texture and hardness sensors. The thesis discusses both touch sensor based and proprioception based systems. Both kinds of systems successfully learned to separate objects of different size and shape as well as individual objects. Several texture and hardness sensor based systems are also discussed. Some of the systems are bimodal, i.e. they merge texture/hardness or touch/proprioception for improved performance. Two novel variants of the Self-Organizing Map that have been developed and used in some of the systems are discussed. <br/><br>
<br/><br>
All systems have been tested and evaluated with different test objects. The systems based on the LUCS Haptic Hand I successfully learned to separate objects of different size. The systems based on the LUCS Haptic Hand II successfully learned to separate objects of different shapes and to discriminate individual objects. The systems based on the anthropomorphic robot hand, the LUCS Haptic Hand III, were all based on proprioceptive information. These systems successfully learned to separate objects of different shapes and sizes. They were also able to discriminate individual objects. The texture and hardness based systems were able to discriminate individual objects and to categorize them as hard or soft. The hardness and texture sensors were also used in a system that successfully developed associated representations of these two submodalities.}},
  author       = {{Johnsson, Magnus}},
  isbn         = {{978-91-977380-3-3}},
  issn         = {{1101-8453}},
  keywords     = {{touch perception; neural network; robot hand; brain model; cognitive model; robot model; tensor network; associative self-organizing map; SOM; proprioception}},
  language     = {{eng}},
  school       = {{Lund University}},
  series       = {{Lund University Cognitive Studies}},
  title        = {{Sense of Touch in Robots}},
  volume       = {{141}},
  year         = {{2009}},
}