Readability of the gaze and expressions of a robot museum visitor: Impact of the low level sensory-motor control
Aliaa Moualla, Ali Karaouzene, Sofiane Boucenna, Denis Vidal, Philippe Gaussier · 2017
In this paper we propose a neural network allowing a mobile robot to learn artwork appreciation. The learning is based on the social referencing approach. The robot acquires its knowledge (artificial taste) from the interaction with humans. We present and analyze specifically the visual system, its impact on the robot behavior, and at the end, we analyze the readability of our robot behavior according to visitors comments. We show that the low level spatial competition between the values associated to areas of interest in the image are important for the coherence of the robot's object evaluation and the readability of its behavior.