Control of a Humanoid NAO Robot by an Adaptive Bioinspired Cerebellar Module in 3D Motion Tasks

Figure 2

Cerebellar SNN and coding/decoding strategies. (a) The computational model applied for creating the cerebellar SNN embedded into the controller of NAO robot. Each block represents a neural population, with the relative inputs and outputs. The excitatory, inhibitory, and teaching connections are depicted. The shaded areas represent the three plasticity sites: magenta the PF-PC synapses, blue the MF-DCN synapses, and green the PC-DCN synapses, adapted from [15]. (b) Coding (for MFs and IOs) and decoding (for DCNs) strategies implemented to integrate the analog robotic world with the spiking activity of the SNN. The 3 joint angles and angular velocities are fed as input to the MFs by means of an RBF approach, overlapped to a random activity. Each joint error is transformed into IO spikes by means of Poisson generators, which produce spikes with a probability that is proportional to the error magnitude. Each IO generates a spike pattern that is therefore independent of their history and of the other IOs. The DCN spikes are transformed into an angular correction sent to the robot joints by means of an instantaneous firing rate computation, subsequently averaged with a mobile-window filter.

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