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

Figure 3

Cortical plasticity optimization. (a) Cost function resulting from the gross exploration of and parameters. Darkest values represent low values of the cost function, therefore the best combinations of the two plasticity parameters. The parameter space further explored in the finer search (b) is identified by the red square. Blue and green crosses identify two examples parameters giving bad performances (c). (b) Cost function resulting from the finer exploration of and parameters. The red square identifies the global minimum, therefore the chosen combination of and . (c) Three examples of RMSE performance across the 30 trials of the protocol. The red line represents a good performance, with a reduction of the RMSE during the acquisition phase and a good extinction in the last 5 trials. The blue line represents the combination of and ; therefore, no correction happened in the acquisition phase, leading to a high cost function value. The green line represents a combination of too high and , leading to an unstable and ineffective correction along the trials. (d) Mean and SD of the RMSE in 10 tests performed with the Webot simulator with the best combination of and identified in the finer exploration.

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