In static scenarios, binaural sound localization is fundamentally limited by front-back ambiguity and distance non-observability.Overthepastfewyears,“active”schemes have been shown to overcome these shortcomings, by combining spatial binaural cues with the motor commands of the sensor. In this context, given a Gaussian prior on the relativepositiontoasource,thispaperdeterminesanadmissible motion of a binaural head which leads, on average, to the one-step-ahead most informative audio-motor localization. To this aim, a constrained optimization problem is set up, which consists in maximizing the entropy of the next predicted measurement probability density function over a cylindric admissible set. The method is appraised through geometrical arguments, and validated in simulations and on real-life robotic experiments.

EB00000003496K

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• An information based feedback control for audio-motor binaural localization