Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the ICAROB International Conference on Artificial Life and Robotics 2017 |
| Number of pages | 4 |
| Publisher | ALife Robotics Co, Ltd. |
| Publication date | 2017 |
| ISBN (Electronic) | 9784990835026 |
| Publication status | Published - 2017 |
| Event | 2017 International Conference on Artificial Life and Robotics - Seagaia Convention Center, Miyazaki, Japan Duration: 19 Jan 2017 → 22 Jan 2017 |
Conference
| Conference | 2017 International Conference on Artificial Life and Robotics |
|---|---|
| Location | Seagaia Convention Center |
| Country | Japan |
| City | Miyazaki |
| Period | 19/01/2017 → 22/01/2017 |
Keywords
- Motor control
- Cerebellum
- Machine learning
- Modular robot
- Internal model
- Adaptive behavior
Cite this
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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 proceeding › Article in proceedings › Research › peer-review
TY - GEN
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
Y1 - 2017
N2 - 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
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
PB - ALife Robotics Co, Ltd.
ER -