Haptic Perception with Self-Organizing ANNs and an Anthropomorphic Robot Hand
(2010) In Journal of Robotics- Abstract
- We have implemented and compared four biologically motivated self-organizing haptic systems based on proprioception. All systems employ a 12-d.o.f. anthropomorphic robot hand, the LUCS Haptic Hand 3. The four systems differ in the kind of self- organizing neural network used for clustering. For the mapping of the explored objects, one system uses a Self-Organizing Map (SOM), one uses a Growing Cell Structure (GCS), one uses a Growing Cell Structure with Deletion of Neurons (GCS-DN), and one uses a Growing Grid (GG). The systems were trained and tested with 10 different objects of different sizes from two different shape categories. The generalization abilities of the systems were tested with 6 new objects. The systems showed good... (More)
- We have implemented and compared four biologically motivated self-organizing haptic systems based on proprioception. All systems employ a 12-d.o.f. anthropomorphic robot hand, the LUCS Haptic Hand 3. The four systems differ in the kind of self- organizing neural network used for clustering. For the mapping of the explored objects, one system uses a Self-Organizing Map (SOM), one uses a Growing Cell Structure (GCS), one uses a Growing Cell Structure with Deletion of Neurons (GCS-DN), and one uses a Growing Grid (GG). The systems were trained and tested with 10 different objects of different sizes from two different shape categories. The generalization abilities of the systems were tested with 6 new objects. The systems showed good performance with the objects from the training set as well as in the generalization experiments. Thus the systems could discriminate individual objects, and they clustered the activities into small cylinders, large cylinders, small blocks, and large blocks. Moreover, the self- organizing ANNs were also organized according to size. The GCS-DN system also evolved disconnected networks representing the different clusters in the input space (small cylinders, large cylinders, small blocks, large blocks), and the generalization samples activated neurons in a proper subnetwork in all but one case. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1652352
- author
- Johnsson, Magnus LU and Balkenius, Christian LU
- organization
- publishing date
- 2010
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of Robotics
- article number
- 860790
- publisher
- Hindawi Limited
- ISSN
- 1687-9600
- DOI
- 10.1155/2010/860790
- project
- Ikaros: An infrastructure for system level modelling of the brain
- language
- English
- LU publication?
- yes
- id
- 1a109c84-7093-4865-9b31-268afee24af1 (old id 1652352)
- date added to LUP
- 2016-04-01 10:01:36
- date last changed
- 2019-09-06 02:18:25
@article{1a109c84-7093-4865-9b31-268afee24af1, abstract = {{We have implemented and compared four biologically motivated self-organizing haptic systems based on proprioception. All systems employ a 12-d.o.f. anthropomorphic robot hand, the LUCS Haptic Hand 3. The four systems differ in the kind of self- organizing neural network used for clustering. For the mapping of the explored objects, one system uses a Self-Organizing Map (SOM), one uses a Growing Cell Structure (GCS), one uses a Growing Cell Structure with Deletion of Neurons (GCS-DN), and one uses a Growing Grid (GG). The systems were trained and tested with 10 different objects of different sizes from two different shape categories. The generalization abilities of the systems were tested with 6 new objects. The systems showed good performance with the objects from the training set as well as in the generalization experiments. Thus the systems could discriminate individual objects, and they clustered the activities into small cylinders, large cylinders, small blocks, and large blocks. Moreover, the self- organizing ANNs were also organized according to size. The GCS-DN system also evolved disconnected networks representing the different clusters in the input space (small cylinders, large cylinders, small blocks, large blocks), and the generalization samples activated neurons in a proper subnetwork in all but one case.}}, author = {{Johnsson, Magnus and Balkenius, Christian}}, issn = {{1687-9600}}, language = {{eng}}, publisher = {{Hindawi Limited}}, series = {{Journal of Robotics}}, title = {{Haptic Perception with Self-Organizing ANNs and an Anthropomorphic Robot Hand}}, url = {{http://dx.doi.org/10.1155/2010/860790}}, doi = {{10.1155/2010/860790}}, year = {{2010}}, }