Projects per year
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, in the form of a Unit Learning Machine. The LWPR algorithm 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-like microcircuit refines the LWPR output delivering corrective commands. We contrasted distinct cerebellar-like circuits including analytical models and spiking models implemented on the SpiNNaker platform, showing promising performance and robustness results.
internal model of a single robot module to command the robot to follow a desired trajectory with its end-effector. The cerebellar-like microcircuit refines the LWPR output delivering corrective commands. We contrasted distinct cerebellar-like circuits including analytical models and spiking models implemented on the SpiNNaker platform, showing promising performance and robustness results.
Original language | English |
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Journal | Journal of Robotics Networks and Artificial Life |
Volume | 4 |
Issue number | 1 |
Pages (from-to) | 62–66 |
Publication status | Published - 2017 |
Keywords
- Motor control
- Cerebellum
- Machine learning
- Modular robot
- Internal model
- Adaptive behavior
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Projects
- 1 Finished
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HBP SGA1: Human Brain Project. Subproject 10 Neurorobotics Platform - SGA1
Tolu, S., Lund, H. H., Capolei, M. C., Corchado Miralles, C. & Baira Ojeda, I.
01/04/2016 → 01/04/2018
Project: Research