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Modular reservoir computing networks for imitation learning of multiple robot behaviors

Tim Waegeman, Eric Antonelo, Francis wyffels UGent and Benjamin Schrauwen (2009) IEEE International symposium on Computational Intelligence in Robotics and Automation, 8th, Proceedings. p.27-32
abstract
Autonomous mobile robots must accomplish tasks in unknown and noisy environments. In this context, learning robot behaviors in an imitation based approach would be desirable in the perspective of service robotics as well as of learning robots. In this work, we use Reservoir Computing (RC) for learning robot behaviors by demonstration. In RC, a randomly generated recurrent neural network, the reservoir, projects the input to a dynamic temporal space. The reservoir states are mapped into a readout output layer which is the solely part being trained using standard linear regression. In this paper, we use a two layered modular structure, where the first layer comprises two RC networks, each one for learning primitive behaviors, namely, obstacle avoidance and target seeking. The second layer is composed of one RC network for behavior combination and coordination. The hierarchical RC network learns by examples given by simple controllers which implement the primitive behaviors. We use a simulation model of the e-puck robot which has distance sensors and a camera that serves as input for our system. The experiments show that, after training, the robot learns to coordinate the Goal Seeking (GS) and the Object Avoidance (OA) behaviors in unknown environments, being able to capture targets and navigate efficiently.
Please use this url to cite or link to this publication:
author
organization
year
type
conference (proceedingsPaper)
publication status
published
subject
keyword
robots, Autonomous mobile robots, Reservoir Computing, Robotics
in
IEEE International symposium on Computational Intelligence in Robotics and Automation, 8th, Proceedings
editor
Keun-Ho Rew
pages
27 - 32
publisher
IEEE
place of publication
New York, NY, USA
conference name
8th IEEE International symposium on Computational Intelligence in Robotics and Automation (CIRA 2009)
conference location
Daejeon, South Korea
conference start
2009-12-15
conference end
2009-12-18
Web of Science type
Proceedings Paper
Web of Science id
000283802800005
ISBN
9781424448081
9781424448098
DOI
10.1109/CIRA.2009.5423194
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
814608
handle
http://hdl.handle.net/1854/LU-814608
date created
2009-12-20 13:08:35
date last changed
2017-01-02 09:52:55
@inproceedings{814608,
  abstract     = {Autonomous mobile robots must accomplish tasks in unknown and noisy environments. In this context, learning robot behaviors in an imitation based approach would be desirable in the perspective of service robotics as well as of learning robots. In this work, we use Reservoir Computing (RC) for learning robot behaviors by demonstration. In RC, a randomly generated recurrent neural network, the reservoir, projects the input to a dynamic temporal space. The reservoir states are mapped into a readout output layer which is the solely part being trained using standard linear regression. In this paper, we use a two layered modular structure, where the first layer comprises two RC networks, each one for learning primitive behaviors, namely, obstacle avoidance and target seeking. The second layer is composed of one RC network for behavior combination and coordination. The hierarchical RC network learns by examples given by simple controllers which implement the primitive behaviors. We use a simulation model of the e-puck robot which has distance sensors and a camera that serves as input for our system. The experiments show that, after training, the robot learns to coordinate the Goal Seeking (GS) and the Object Avoidance (OA) behaviors in unknown environments, being able to capture targets and navigate efficiently.},
  author       = {Waegeman, Tim and Antonelo, Eric and wyffels, Francis and Schrauwen, Benjamin},
  booktitle    = {IEEE International symposium on Computational Intelligence in Robotics and Automation, 8th, Proceedings},
  editor       = {Rew, Keun-Ho},
  isbn         = {9781424448081},
  keyword      = {robots,Autonomous mobile robots,Reservoir Computing,Robotics},
  language     = {eng},
  location     = {Daejeon, South Korea},
  pages        = {27--32},
  publisher    = {IEEE},
  title        = {Modular reservoir computing networks for imitation learning of multiple robot behaviors},
  url          = {http://dx.doi.org/10.1109/CIRA.2009.5423194},
  year         = {2009},
}

Chicago
Waegeman, Tim, Eric Antonelo, Francis wyffels, and Benjamin Schrauwen. 2009. “Modular Reservoir Computing Networks for Imitation Learning of Multiple Robot Behaviors.” In IEEE International Symposium on Computational Intelligence in Robotics and Automation, 8th, Proceedings, ed. Keun-Ho Rew, 27–32. New York, NY, USA: IEEE.
APA
Waegeman, T., Antonelo, E., wyffels, F., & Schrauwen, B. (2009). Modular reservoir computing networks for imitation learning of multiple robot behaviors. In K.-H. Rew (Ed.), IEEE International symposium on Computational Intelligence in Robotics and Automation, 8th, Proceedings (pp. 27–32). Presented at the 8th IEEE International symposium on Computational Intelligence in Robotics and Automation (CIRA 2009), New York, NY, USA: IEEE.
Vancouver
1.
Waegeman T, Antonelo E, wyffels F, Schrauwen B. Modular reservoir computing networks for imitation learning of multiple robot behaviors. In: Rew K-H, editor. IEEE International symposium on Computational Intelligence in Robotics and Automation, 8th, Proceedings. New York, NY, USA: IEEE; 2009. p. 27–32.
MLA
Waegeman, Tim, Eric Antonelo, Francis wyffels, et al. “Modular Reservoir Computing Networks for Imitation Learning of Multiple Robot Behaviors.” IEEE International Symposium on Computational Intelligence in Robotics and Automation, 8th, Proceedings. Ed. Keun-Ho Rew. New York, NY, USA: IEEE, 2009. 27–32. Print.