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Terrain classification for a quadruped robot

Jonas Degrave UGent, Robin Van Cauwenbergh, Francis wyffels UGent, Tim Waegeman and Benjamin Schrauwen (2013) 2013 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2013), VOL 1. p.185-190
abstract
Using data retrieved from the Puppy II robot at the University of Zurich (UZH), we show that machine learning techniques with non-linearities and fading memory are effective for terrain classification, both supervised and unsupervised, even with a limited selection of input sensors. The results indicate that most information for terrain classification is found in the combination of tactile sensors and proprioceptive joint angle sensors. The classification error is small enough to have a robot adapt the gait to the terrain and hence move more robustly.
Please use this url to cite or link to this publication:
author
organization
year
type
conference (proceedingsPaper)
publication status
published
subject
keyword
proprioception, reservoir, computing, classification, quadruped robot, INDEPENDENT COMPONENT ANALYSIS, terrain
in
2013 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2013), VOL 1
pages
185 - 190
publisher
IEEE
conference name
12th International Conference on Machine Learning and Applications (ICMLA)
conference location
Miami, Florida, USA
conference start
2013-12-04
conference end
2013-12-07
Web of Science type
Proceedings Paper
Web of Science id
000353637800031
ISBN
9780769551449
DOI
10.1109/ICMLA.2013.39
project
AMARSI (Adaptive Modular Architecture for Rich Motor Skills)
language
English
UGent publication?
yes
classification
P1
copyright statement
I have retained and own the full copyright for this publication
id
4215673
handle
http://hdl.handle.net/1854/LU-4215673
date created
2013-12-23 11:44:48
date last changed
2016-12-19 15:37:45
@inproceedings{4215673,
  abstract     = {Using data retrieved from the Puppy II robot at the University of Zurich (UZH), we show that machine learning techniques with non-linearities and fading memory are effective for terrain classification, both supervised and unsupervised, even with a limited selection of input sensors. The results indicate that most information for terrain classification is found in the combination of tactile sensors and proprioceptive joint angle sensors. The classification error is small enough to have a robot adapt the gait to the terrain and hence move more robustly.},
  author       = {Degrave, Jonas and Van Cauwenbergh, Robin and wyffels, Francis and Waegeman, Tim and Schrauwen, Benjamin},
  booktitle    = {2013 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2013), VOL 1},
  isbn         = {9780769551449},
  keyword      = {proprioception,reservoir,computing,classification,quadruped robot,INDEPENDENT COMPONENT ANALYSIS,terrain},
  language     = {eng},
  location     = {Miami, Florida, USA},
  pages        = {185--190},
  publisher    = {IEEE},
  title        = {Terrain classification for a quadruped robot},
  url          = {http://dx.doi.org/10.1109/ICMLA.2013.39},
  year         = {2013},
}

Chicago
Degrave, Jonas, Robin Van Cauwenbergh, Francis wyffels, Tim Waegeman, and Benjamin Schrauwen. 2013. “Terrain Classification for a Quadruped Robot.” In 2013 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2013), VOL 1, 185–190. IEEE.
APA
Degrave, J., Van Cauwenbergh, R., wyffels, F., Waegeman, T., & Schrauwen, B. (2013). Terrain classification for a quadruped robot. 2013 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2013), VOL 1 (pp. 185–190). Presented at the 12th International Conference on Machine Learning and Applications (ICMLA), IEEE.
Vancouver
1.
Degrave J, Van Cauwenbergh R, wyffels F, Waegeman T, Schrauwen B. Terrain classification for a quadruped robot. 2013 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2013), VOL 1. IEEE; 2013. p. 185–90.
MLA
Degrave, Jonas, Robin Van Cauwenbergh, Francis wyffels, et al. “Terrain Classification for a Quadruped Robot.” 2013 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2013), VOL 1. IEEE, 2013. 185–190. Print.