Neural network models of haptic shape perception
(2007) In Robotics and Autonomous Systems 55(9). p.720-727- Abstract
- Three different models of tactile shape perception inspired by the human haptic system were tested using an 8 d.o.f. robot hand with 45 tactile sensors. One model is based on the tensor product of different proprioceptive and tactile signals and a self-organizing map (SOM). The two other models replace the tensor product operation with a novel self-organizing neural network, the Tensor-Multiple Peak Self-Organizing Map (T-MPSOM). The two T-MPSOM models differ in the procedure employed to calculate the neural activation. The computational models were trained and tested with a set of objects consisting of hard spheres, blocks and cylinders. All the models learned to map different shapes to different areas of the SOM, and the tensor product... (More)
- Three different models of tactile shape perception inspired by the human haptic system were tested using an 8 d.o.f. robot hand with 45 tactile sensors. One model is based on the tensor product of different proprioceptive and tactile signals and a self-organizing map (SOM). The two other models replace the tensor product operation with a novel self-organizing neural network, the Tensor-Multiple Peak Self-Organizing Map (T-MPSOM). The two T-MPSOM models differ in the procedure employed to calculate the neural activation. The computational models were trained and tested with a set of objects consisting of hard spheres, blocks and cylinders. All the models learned to map different shapes to different areas of the SOM, and the tensor product model as well as one of the T-MPSOM models also learned to discriminate individual test objects. (c) 2007 Elsevier B.V. All rights reserved. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/655858
- author
- Johnsson, Magnus LU and Balkenius, Christian LU
- organization
- publishing date
- 2007
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- robotic hand, tensor product, haptic perception, self-organizing map
- in
- Robotics and Autonomous Systems
- volume
- 55
- issue
- 9
- pages
- 720 - 727
- publisher
- Elsevier
- external identifiers
-
- wos:000249771900009
- scopus:34548029725
- ISSN
- 0921-8890
- DOI
- 10.1016/j.robot.2007.05.003
- project
- Ikaros: An infrastructure for system level modelling of the brain
- language
- English
- LU publication?
- yes
- id
- 02be64a9-3154-48f8-8957-7203e3698e91 (old id 655858)
- date added to LUP
- 2016-04-01 16:10:09
- date last changed
- 2022-01-28 17:44:39
@article{02be64a9-3154-48f8-8957-7203e3698e91, abstract = {{Three different models of tactile shape perception inspired by the human haptic system were tested using an 8 d.o.f. robot hand with 45 tactile sensors. One model is based on the tensor product of different proprioceptive and tactile signals and a self-organizing map (SOM). The two other models replace the tensor product operation with a novel self-organizing neural network, the Tensor-Multiple Peak Self-Organizing Map (T-MPSOM). The two T-MPSOM models differ in the procedure employed to calculate the neural activation. The computational models were trained and tested with a set of objects consisting of hard spheres, blocks and cylinders. All the models learned to map different shapes to different areas of the SOM, and the tensor product model as well as one of the T-MPSOM models also learned to discriminate individual test objects. (c) 2007 Elsevier B.V. All rights reserved.}}, author = {{Johnsson, Magnus and Balkenius, Christian}}, issn = {{0921-8890}}, keywords = {{robotic hand; tensor product; haptic perception; self-organizing map}}, language = {{eng}}, number = {{9}}, pages = {{720--727}}, publisher = {{Elsevier}}, series = {{Robotics and Autonomous Systems}}, title = {{Neural network models of haptic shape perception}}, url = {{http://dx.doi.org/10.1016/j.robot.2007.05.003}}, doi = {{10.1016/j.robot.2007.05.003}}, volume = {{55}}, year = {{2007}}, }