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Sense of Touch in Robots with Self-Organizing Maps

Johnsson, Magnus LU and Balkenius, Christian LU orcid (2011) In IEEE Transactions on Robotics 27(3). p.498-507
Abstract
We review a number of self-organizing-robot systems that are able to extract features from haptic sensory information. They are all based on self-organizing maps (SOMs). First, we describe a number of systems based on the three-fingered-robot hand, i.e., the Lund University Cognitive Science (LUCS) Haptic-Hand II, that successfully extracts the shapes of objects. These systems explore each object with a sequence of grasps while superimposing the information from individual grasps after cross-coding proprioceptive information for different parts of the hand and the registrations of tactile sensors. The cross-coding is done by employing either the tensor-product operation or a novel self-organizing neural network called the tensor multiple... (More)
We review a number of self-organizing-robot systems that are able to extract features from haptic sensory information. They are all based on self-organizing maps (SOMs). First, we describe a number of systems based on the three-fingered-robot hand, i.e., the Lund University Cognitive Science (LUCS) Haptic-Hand II, that successfully extracts the shapes of objects. These systems explore each object with a sequence of grasps while superimposing the information from individual grasps after cross-coding proprioceptive information for different parts of the hand and the registrations of tactile sensors. The cross-coding is done by employing either the tensor-product operation or a novel self-organizing neural network called the tensor multiple peak SOM (T-MPSOM). Second, we present a system based on proprioception that uses an anthropomorphic robot hand, i.e., the LUCS haptic-hand III. This system is able to distinguish objects both according to shape and size. Third, we present systems that are able to extract and combine the texture and hardness properties from explored materials. (Less)
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author
and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Cognitive robotics, manipulators, self-organizing feature maps, tactile sensors, unsupervised learning
in
IEEE Transactions on Robotics
volume
27
issue
3
pages
498 - 507
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • wos:000291404600011
  • scopus:79958772732
ISSN
1941-0468
DOI
10.1109/TRO.2011.2130090
project
Ikaros: An infrastructure for system level modelling of the brain
Thinking in Time: Cognition, Communication and Learning
language
English
LU publication?
yes
id
accc0d19-4646-4799-af48-04f8e23a9bef (old id 1982534)
date added to LUP
2016-04-01 10:27:02
date last changed
2022-01-25 23:20:38
@article{accc0d19-4646-4799-af48-04f8e23a9bef,
  abstract     = {{We review a number of self-organizing-robot systems that are able to extract features from haptic sensory information. They are all based on self-organizing maps (SOMs). First, we describe a number of systems based on the three-fingered-robot hand, i.e., the Lund University Cognitive Science (LUCS) Haptic-Hand II, that successfully extracts the shapes of objects. These systems explore each object with a sequence of grasps while superimposing the information from individual grasps after cross-coding proprioceptive information for different parts of the hand and the registrations of tactile sensors. The cross-coding is done by employing either the tensor-product operation or a novel self-organizing neural network called the tensor multiple peak SOM (T-MPSOM). Second, we present a system based on proprioception that uses an anthropomorphic robot hand, i.e., the LUCS haptic-hand III. This system is able to distinguish objects both according to shape and size. Third, we present systems that are able to extract and combine the texture and hardness properties from explored materials.}},
  author       = {{Johnsson, Magnus and Balkenius, Christian}},
  issn         = {{1941-0468}},
  keywords     = {{Cognitive robotics; manipulators; self-organizing feature maps; tactile sensors; unsupervised learning}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{498--507}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Transactions on Robotics}},
  title        = {{Sense of Touch in Robots with Self-Organizing Maps}},
  url          = {{http://dx.doi.org/10.1109/TRO.2011.2130090}},
  doi          = {{10.1109/TRO.2011.2130090}},
  volume       = {{27}},
  year         = {{2011}},
}