Abstract-Exploratory activities seem to be intrinsically rewarding for children and crucial for their cognitive development. Can a machine be endowed with such an intrinsic motivation system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development. The complexity of the robot's activities autonomously increases and complex developmental sequences self-organize without being constructed in a supervised manner. Two experiments are presented illustrating the stage-like organization emerging with this mechanism. In one of them, a physical robot is placed on a baby play mat with objects that it can learn to manipulate. Experimental results show that the robot first spends time in situations which are easy to learn, then shifts its attention progressively to situations of increasing difficulty, avoiding situations in which nothing can be learned. Finally, these various results are discussed in relation to more complex forms of behavioral organization and data coming from developmental psychology.

Intrinsic motivation, centrally involved in spontaneous exploration and curiosity, is a crucial concept in developmental psychology. It has been argued to be a crucial mechanism for open-ended cognitive development in humans, and as such has gathered a growing interest from developmental roboticists in the recent years. The goal of this paper is threefold. First, it provides a synthesis of the different approaches of intrinsic motivation in psychology. Second, by interpreting these approaches in a computational reinforcement learning framework, we argue that they are not operational and even sometimes inconsistent. Third, we set the ground for a systematic operational study of intrinsic motivation by presenting a formal typology of possible computational approaches. This typology is partly based on existing computational models, but also presents new ways of conceptualizing intrinsic motivation. We argue that this kind of computational typology might be useful for opening new avenues for research both in psychology and developmental robotics.Keywords: intrinsic motivation, cognitive development, reward, reinforcement learning, exploration, curiosity, computational modeling, artifi cial intelligence, developmental robotics INTRODUCTIONThere exists a wide diversity of motivation systems in living organisms, and humans in particular. For example, there are systems that push the organism to maintain certain levels of chemical energy, involving the ingestion of food, or systems that push the organism to maintain its temperature or its physical integrity in a zone of viability. Inspired by these kinds of motivation and their understanding by (neuro-) ethologists, roboticists have built machines endowed with similar systems with the aim of providing them with autonomy and properties of life-like intelligence (Arkin, 2005). For example sowbug-inspired robots (Endo and Arkin, 2001), praying mantis robots (Arkin et al., 1998) dog-like robots (Fujita et al., 2001) have been constructed. Some animals, and this is most prominent in humans, also have more general motivations that push them to explore, manipulate or probe their environment, fostering curiosity and engagement in playful and new activities. This kind of motivation, which is called intrinsic motivation by psychologists (Ryan and Deci, 2000), is paramount for sensorimotor and cognitive development throughout lifespan. There is a vast literature in psychology that explains why it is essential for cognitive growth and organization, and investigates the actual potential cognitive processes underlying intrinsic motivation (Berlyne, 1960;Csikszentmihalyi, 1991;Deci and Ryan, 1985;Ryan and Deci, 2000;White, 1959). This has gathered the interest of a growing number of researchers in developmental robotics in the recent years, and several computational models have been developed (see Barto et al., 2004; Oudeyer et al., 2007 for reviews).However, the very concept of intrinsic motivation has never really been consistently and critically discussed from a computational point ...

11Received Accepted 13Are robots perceived in the same manner in the West and in Japan? This article presents a preliminary exploration of several aspects of the Japanese culture and a survey of 15 the most important myths and novels involving artificial beings in Western literature. Through this analysis, the article tries to shed light on particular cultural features that 17 may account for contemporary differences in our behavior towards humanoids.

In recent years there have been multiple successful attempts tackling document processing problems separately by designing task specific hand-tuned strategies. We argue that the diversity of historical document processing tasks prohibits to solve them one at a time and shows a need for designing generic approaches in order to handle the variability of historical series. In this paper, we address multiple tasks simultaneously such as page extraction, baseline extraction, layout analysis or multiple typologies of illustrations and photograph extraction.We propose an open-source implementation of a CNN-based pixel-wise predictor coupled with task dependent post-processing blocks. We show that a single CNN-architecture can be used across tasks with competitive results. Moreover most of the task-specific post-precessing steps can be decomposed in a small number of simple and standard reusable operations, adding to the flexibility of our approach.

Children seem to acquire new know-how in a continuous and open-ended manner. In this paper, we hypothesize that an intrinsic motivation to progress in learning is at the origins of the remarkable structure of children's developmental trajectories. In this view, children engage in exploratory and playful activities for their own sake, not as steps toward other extrinsic goals. The central hypothesis of this paper is that intrinsically motivating activities correspond to expected decrease in prediction error. This motivation system pushes the infant to avoid both predictable and unpredictable situations in order to focus on the ones that are expected to maximize progress in learning. Based on a computational model and a series of robotic experiments, we show how this principle can lead to organized sequences of behavior of increasing complexity characteristic of several behavioral and developmental patterns observed in humans. We then discuss the putative circuitry underlying such an intrinsic motivation system in the brain and formulate two novel hypotheses. The first one is that tonic dopamine acts as a learning progress signal. The second is that this progress signal is directly computed through a hierarchy of microcortical circuits that act both as prediction and metaprediction systems.