A Combination of Machine Learning and Cerebellar Models for the Motor Control and Learning of a Modular Robot

Ismael Baira Ojeda, Silvia Tolu, Moises Pacheco, David Johan Christensen, Henrik Hautop Lund

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

We scaled up a bio-inspired control architecture for the motor control and motor learning of a real modular robot. In our approach, the Locally Weighted Projection Regression algorithm (LWPR) and a cerebellar microcircuit coexist, forming a Unit Learning Machine. The LWPR optimizes the input space and learns the internal model of a single robot module to command the robot to follow a desired trajectory with its end-effector. The cerebellar microcircuit refines the LWPR output delivering corrective commands. We contrasted distinct cerebellar circuits including analytical models and spiking models implemented on the SpiNNaker platform, showing promising performance and robustness results
Original languageEnglish
Title of host publicationProceedings of the ICAROB International Conference on Artificial Life and Robotics 2017
Number of pages4
PublisherALife Robotics Co, Ltd.
Publication date2017
ISBN (Electronic)9784990835026
Publication statusPublished - 2017
Event2017 International Conference on Artificial Life and Robotics - Seagaia Convention Center, Miyazaki, Japan
Duration: 19 Jan 201722 Jan 2017

Conference

Conference2017 International Conference on Artificial Life and Robotics
LocationSeagaia Convention Center
CountryJapan
CityMiyazaki
Period19/01/201722/01/2017

Keywords

  • Motor control
  • Cerebellum
  • Machine learning
  • Modular robot
  • Internal model
  • Adaptive behavior

Cite this

Baira Ojeda, I., Tolu, S., Pacheco, M., Christensen, D. J., & Lund, H. H. (2017). A Combination of Machine Learning and Cerebellar Models for the Motor Control and Learning of a Modular Robot. In Proceedings of the ICAROB International Conference on Artificial Life and Robotics 2017 ALife Robotics Co, Ltd..
Baira Ojeda, Ismael ; Tolu, Silvia ; Pacheco, Moises ; Christensen, David Johan ; Lund, Henrik Hautop . / A Combination of Machine Learning and Cerebellar Models for the Motor Control and Learning of a Modular Robot. Proceedings of the ICAROB International Conference on Artificial Life and Robotics 2017. ALife Robotics Co, Ltd., 2017.
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abstract = "We scaled up a bio-inspired control architecture for the motor control and motor learning of a real modular robot. In our approach, the Locally Weighted Projection Regression algorithm (LWPR) and a cerebellar microcircuit coexist, forming a Unit Learning Machine. The LWPR optimizes the input space and learns the internal model of a single robot module to command the robot to follow a desired trajectory with its end-effector. The cerebellar microcircuit refines the LWPR output delivering corrective commands. We contrasted distinct cerebellar circuits including analytical models and spiking models implemented on the SpiNNaker platform, showing promising performance and robustness results",
keywords = "Motor control, Cerebellum, Machine learning, Modular robot, Internal model, Adaptive behavior",
author = "{Baira Ojeda}, Ismael and Silvia Tolu and Moises Pacheco and Christensen, {David Johan} and Lund, {Henrik Hautop}",
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Baira Ojeda, I, Tolu, S, Pacheco, M, Christensen, DJ & Lund, HH 2017, A Combination of Machine Learning and Cerebellar Models for the Motor Control and Learning of a Modular Robot. in Proceedings of the ICAROB International Conference on Artificial Life and Robotics 2017. ALife Robotics Co, Ltd., 2017 International Conference on Artificial Life and Robotics , Miyazaki, Japan, 19/01/2017.

A Combination of Machine Learning and Cerebellar Models for the Motor Control and Learning of a Modular Robot. / Baira Ojeda, Ismael; Tolu, Silvia; Pacheco, Moises; Christensen, David Johan; Lund, Henrik Hautop .

Proceedings of the ICAROB International Conference on Artificial Life and Robotics 2017. ALife Robotics Co, Ltd., 2017.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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T1 - A Combination of Machine Learning and Cerebellar Models for the Motor Control and Learning of a Modular Robot

AU - Baira Ojeda, Ismael

AU - Tolu, Silvia

AU - Pacheco, Moises

AU - Christensen, David Johan

AU - Lund, Henrik Hautop

PY - 2017

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AB - We scaled up a bio-inspired control architecture for the motor control and motor learning of a real modular robot. In our approach, the Locally Weighted Projection Regression algorithm (LWPR) and a cerebellar microcircuit coexist, forming a Unit Learning Machine. The LWPR optimizes the input space and learns the internal model of a single robot module to command the robot to follow a desired trajectory with its end-effector. The cerebellar microcircuit refines the LWPR output delivering corrective commands. We contrasted distinct cerebellar circuits including analytical models and spiking models implemented on the SpiNNaker platform, showing promising performance and robustness results

KW - Motor control

KW - Cerebellum

KW - Machine learning

KW - Modular robot

KW - Internal model

KW - Adaptive behavior

M3 - Article in proceedings

BT - Proceedings of the ICAROB International Conference on Artificial Life and Robotics 2017

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Baira Ojeda I, Tolu S, Pacheco M, Christensen DJ, Lund HH. A Combination of Machine Learning and Cerebellar Models for the Motor Control and Learning of a Modular Robot. In Proceedings of the ICAROB International Conference on Artificial Life and Robotics 2017. ALife Robotics Co, Ltd. 2017