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Sensor fusion for robot control through deep reinforcement learning

Steven Bohez UGent, Tim Verbelen UGent, Elias De Coninck UGent, Bert Vankeirsbilck UGent, Pieter Simoens UGent and Bart Dhoedt UGent (2017) 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS). In IEEE International Conference on Intelligent Robots and Systems p.2365-2370
abstract
Deep reinforcement learning is becoming increasingly popular for robot control algorithms, with the aim for a robot to self-learn useful feature representations from unstructured sensory input leading to the optimal actuation policy. In addition to sensors mounted on the robot, sensors might also be deployed in the environment, although these might need to be accessed via an unreliable wireless connection. In this paper, we demonstrate deep neural network architectures that are able to fuse information generated by multiple sensors and are robust to sensor failures at runtime. We evaluate our method on a search and pick task for a robot both in simulation and the real world.
Please use this url to cite or link to this publication:
author
organization
year
type
conference (proceedingsPaper)
publication status
published
in
2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
series title
IEEE International Conference on Intelligent Robots and Systems
pages
6 pages
publisher
Ieee
place of publication
New york
conference name
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
conference location
Vancouver, CANADA
conference start
2017-09-24
conference end
2017-09-28
Web of Science type
Proceedings Paper
Web of Science id
000426978202073
ISSN
2153-0858
ISBN
978-1-5386-2682-5
language
English
UGent publication?
yes
classification
P1
id
8558393
handle
http://hdl.handle.net/1854/LU-8558393
date created
2018-04-05 06:30:09
date last changed
2018-05-15 12:34:08
@inproceedings{8558393,
  abstract     = {Deep reinforcement learning is becoming increasingly popular for robot control algorithms, with the aim for a robot to self-learn useful feature representations from unstructured sensory input leading to the optimal actuation policy. In addition to sensors mounted on the robot, sensors might also be deployed in the environment, although these might need to be accessed via an unreliable wireless connection. In this paper, we demonstrate deep neural network architectures that are able to fuse information generated by multiple sensors and are robust to sensor failures at runtime. We evaluate our method on a search and pick task for a robot both in simulation and the real world.},
  author       = {Bohez, Steven and Verbelen, Tim and De Coninck, Elias and Vankeirsbilck, Bert and Simoens, Pieter and Dhoedt, Bart},
  booktitle    = {2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)},
  isbn         = {978-1-5386-2682-5},
  issn         = {2153-0858},
  language     = {eng},
  location     = {Vancouver, CANADA},
  pages        = {2365--2370},
  publisher    = {Ieee},
  title        = {Sensor fusion for robot control through deep reinforcement learning},
  year         = {2017},
}

Chicago
Bohez, Steven, Tim Verbelen, Elias De Coninck, Bert Vankeirsbilck, Pieter Simoens, and Bart Dhoedt. 2017. “Sensor Fusion for Robot Control Through Deep Reinforcement Learning.” In 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2365–2370. New york: Ieee.
APA
Bohez, S., Verbelen, T., De Coninck, E., Vankeirsbilck, B., Simoens, P., & Dhoedt, B. (2017). Sensor fusion for robot control through deep reinforcement learning. 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (pp. 2365–2370). Presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), New york: Ieee.
Vancouver
1.
Bohez S, Verbelen T, De Coninck E, Vankeirsbilck B, Simoens P, Dhoedt B. Sensor fusion for robot control through deep reinforcement learning. 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS). New york: Ieee; 2017. p. 2365–70.
MLA
Bohez, Steven, Tim Verbelen, Elias De Coninck, et al. “Sensor Fusion for Robot Control Through Deep Reinforcement Learning.” 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS). New york: Ieee, 2017. 2365–2370. Print.