We present a model of the self-calibration of active binocular vision comprising the simultaneous learning of visual representations, vergence, and pursuit eye movements. The model follows the principle of Active Efficient Coding (AEC), a recent extension of the classic Efficient Coding Hypothesis to active perception. In contrast to previous AEC models, the present model uses deep autoencoders to learn sensory representations. We also propose a new formulation of the intrinsic motivation signal that guides the learning of behavior. We demonstrate the performance of the model in simulations.
@article{arxiv.2101.11391,
title = {Self-Calibrating Active Binocular Vision via Active Efficient Coding with Deep Autoencoders},
author = {Charles Wilmot and Bertram E. Shi and Jochen Triesch},
journal= {arXiv preprint arXiv:2101.11391},
year = {2021}
}