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

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.

Computational Modelling of Cerebellar Magnetic Stimulation: The Effect of Washout

“…The SNN models has three plasticity sites, at cortical level (PF-PC) and at nuclear level, between MF-DCN and PC-DCN, all based on different kinds of Spike-Timing Dependent Plasticity (STDP)[6,11]. PF-PC plasticity is modulated by IO activity, MF-DCN by PC activity, while PC-DCN is a standard unsupervised STDP learning, depending only on the difference between the pre-and post-synaptic firing times[8,15,18]. Each learning rule encompasses two different plasticity mechanisms: Long Term Depression (LTD), decreasing the synapse strength, and Long Term Potentiation (LTP), strengthening the connection.…”

The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach

“…While we have used an open loop control and a target endpoint, models from the neuro-robotics community (e.g. [26,85,86]) typically use feedback control, which ensures that the desired endpoint will be reached, while a trajectory planner sets up the desired joint angles and the according velocities. In those approaches, models representing the cerebellum are embedded in the circuitry as forward and inverse models, and help to bring the actual trajectory closer to the desired trajectory.…”

“…Like with the basal ganglia, most models of the cerebellum focus on internal dynamics but are rarely applied to complex motor tasks. Those computational models of the cerebellum involved in motor tasks have been mainly developed in the context of neurorobotics, often abstract much from biological detail and typically implement a closed-loop motor control network [25][26][27][28][29][30][31].…”