Abstract-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.Index Terms-Artificial intelligence, neural networks, Human robot interactions, computer vision.

International audienceIn this paper, we propose a bio-inspired and developmental neural model allowing a robot, after learning its own dynamics during a babbling phase, to gain imitative and shape recognition abilities leading to early attempts for physical and social interactions. We use a motor controller based on oscillators. During the babbling step, the robot learn to associate its motor primitives (oscillators) to the visual optical flow induced by its own arm. It also statically learn to recognize its arm by selecting moving local view (feature points) in the visual field. We demonstrate in real indoor experiments that, using this same model, early physical (reaching objects) and social (immediate imitation) interactions can emerge through visual ambiguities induced by the external visual stimuli

In this work, we study how learning in a special environment such as a museum can influence the behavior of robots. More specifically, we show that online learning based on interaction with people at a museum leads the robots to develop individual preferences. We first developed a humanoid robot (Berenson) that has the ability to head toward its preferred object and to make a facial expression that corresponds to its attitude toward said object. The robot is programmed with a biologically-inspired neural network sensory-motor architecture. This architecture allows Berenson to learn and to evaluate objects. During experiments, museum visitors’ emotional responses to artworks were recorded and used to build a database for training. A similar database was created in the laboratory with laboratory objects. We use those databases to train two simulated populations of robots. Each simulated robot emulates the Berenson sensory-motor architecture. Firstly, the results show the good performance of our architecture in artwork recognition in the museum. Secondly, they demonstrate the effect of training variability on preference diversity. The response of the two populations in a new unknown environment is different; the museum population of robots shows a greater variance in preferences than the population of robots that have been trained only on laboratory objects. The obtained diversity increases the chances of success in an unknown environment and could favor an accidental discovery.