A Combination of Machine Learning and Cerebellar-like Neural Networks for the Motor Control and Motor Learning of the Fable Modular Robot

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

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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.
Original languageEnglish
JournalJournal of Robotics Networks and Artificial Life
Issue number1
Pages (from-to)62–66
Publication statusPublished - 2017


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

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