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The quest for a brain-inspired learning algorithm for robots has culminatedin the free energy principle from neuroscience that models the brain'sperception and action as an optimization over its free energy objectives. Basedon this idea, we propose an estimation algorithm for accurate output predictionof a quadrotor flying under unmodelled wind conditions. The key idea behindthis work is the handling of unmodelled wind dynamics and the model'snon-linearity errors as coloured noise in the system, and leveraging it foraccurate output predictions. This paper provides the first experimentalvalidation for the usefulness of generalized coordinates for robot perceptionusing Dynamic Expectation Maximization (DEM). Through real flight experiments,we show that the estimator outperforms classical estimators with the leasterror in output predictions. Based on the experimental results, we extend theDEM algorithm for model order selection for complete black box identification.With this paper, we provide the first experimental validation of DEM applied torobot learning.

A Brain Inspired Learning Algorithm for the Perception of a Quadrotor in Wind