Conference paper
A Combination of Machine Learning and Cerebellar Models for the Motor Control and Learning of a Modular Robot
Department of Electrical Engineering, Technical University of Denmark1
Centre for Playware, Centers, Technical University of Denmark2
Automation and Control, Department of Electrical Engineering, Technical University of Denmark3
Copenhagen Center for Health Technology, Centers, Technical University of Denmark4
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
Language: | English |
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Publisher: | ALife Robotics Co, Ltd. |
Year: | 2017 |
Proceedings: | 2017 International Conference on Artificial Life and Robotics |
ISBN: | 4990835026 and 9784990835026 |
Types: | Conference paper |
ORCIDs: | Baira Ojeda, Ismael , Tolu, Silvia , Christensen, David Johan and Lund, Henrik Hautop |