Title: Exploring to learn visual saliency: The RL-IAC approach,
Abstract: The problem of object localization and recognition on autonomous mobile
robots is still an active topic. In this context, we tackle the problem of
learning a model of visual saliency directly on a robot. This model, learned
and improved on-the-fly during the robot's exploration provides an efficient
tool for localizing relevant objects within their environment. The proposed
approach includes two intertwined components. On the one hand, we describe a
method for learning and incrementally updating a model of visual saliency from
a depth-based object detector. This model of saliency can also be exploited to
produce bounding box proposals around objects of interest. On the other hand,
we investigate an autonomous exploration technique to efficiently learn such a
saliency model. The proposed exploration, called Reinforcement
Learning-Intelligent Adaptive Curiosity (RL-IAC) is able to drive the robot's
exploration so that samples selected by the robot are likely to improve the
current model of saliency. We then demonstrate that such a saliency model
learned directly on a robot outperforms several state-of-the-art saliency
techniques, and that RL-IAC can drastically decrease the required time for
learning a reliable saliency model.