We present a responsibility predictor, based on the adaptive filter model of the cerebellum, to provide feed-forward selection of cerebellar calibration models for robot Sound Source Localization (SSL),
based on audio features extracted from the received audio stream. In previous work we described a system that selects the models based on sensory feedback, however, a drawback of that system is that it is only able to select a set of calibrators a-posteriori, after action (e.g. orienting a camera toward the sound source after a position estimate is made). The responsibility predictor improved the system performance compared to that without responsibility prediction. We show that a trained responsibility predictor is able to use contextual signals in the absence of ground truth to successfully select models with a performance approaching that
of a system with full access to the ground truth through sensory feedback
