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

Abstract — Mobile robots often have to make decisions on where to point their sensors, which have limited range and coverage. A good sensing strategy allows the robot to collect useful information for its tasks. Most existing solutions to this active sensing problem choose the direction that maximally reduces the uncertainty in a single state variable. In more complex problem domains, however, uncertainties exist in multiple state variables, and they affect the performance of the robot in different ways. The robot thus needs to have more sophisticated sensing strategies in order to decide which uncertainties to reduce, and to make the correct trade-offs. In this work, we apply least squares reinforcement learning methods to solve this problem. We implemented and tested the learning approach in the RoboCup domain, where the robot attempts to reach a ball and accurately kick it into the goal. We present experimental results that suggest our approach is able to learn highly effective sensing strategies. I.

We view scenes in the real world by moving our eyes three to four times each second, and integrating information across subsequent fixations (foveation points). By taking advantage of this fact, in this paper we propose an original approach to partitioning of a video into shots based on a foveated representation of the video. More precisely, the shotchange detection method is related to the computation, at each time instant, of a consistency measure of the fixation sequences generated by an ideal observer looking at the video. The proposed scheme aims at detecting both abrupt and gradual transitions between shots using a single technique, rather than a set of dedicated methods. Results on videos of various content types are reported and validate the proposed approach Index Terms--- Attentive Vision, Video Segmentation, Shot Detection, Hard Cuts, Dissolves.

Active object tracking, for example, in surveillance tasks, becomes more and more important these days. Besides the tracking algorithms themselves methodologies have to be developed for reasonable active control of the degrees of freedom of all involved cameras. In this paper we present an information theoretic approach that allows the optimal selection of the focal lengths of two cameras during active 3–D object tracking. The selection is based on the uncertainty in the 3–D estimation. This allows us to resolve the trade–off between small and large focal length: in the former case, the chance is increased to keep the object in the field of view of the cameras. In the latter one, 3–D estimation becomes more reliable. Also, more details are provided, for example for recognizing the objects. Beyond a rigorous mathematical framework we present real–time experiments demonstrating that we gain an improvement in 3–D trajectory estimation by up to 42 % in comparison with tracking using a fixed focal length. 1.

Abstract—For the real-time recognition of unspecified gestures by an arbitrary person, a comprehensive framework is presented that addresses two important problems in gesture recognition systems: selective attention and processing frame rate. To address the first problem, we propose the Quadruple Visual Interest Point Strategy. No assumptions are made with regard to scale or rotation of visual features, which are computed from dynamically changing regions of interest in a given image sequence. In this paper, each of the visual features is referred to as a visual interest point, to which a probability density function is assigned, and the selection is carried out. To address the second problem, we developed a selective control method to equip the recognition system with self-load monitoring and controlling functionality. Through evaluation experiments, we show that our approach provides robust recognition with respect to such factors as type of clothing, type of gesture, extent of motion trajectories, and individual differences in motion characteristics. In order to indicate the real-time performance and utility aspects of our approach, a gesture video system is developed that demonstrates full video-rate interaction with displayed image objects. Index Terms—Gesture recognition, selective control, visual interest points, Gaussian density feature, real-time interaction. 1

Abstract—Many information fusion applications are often characterized by a high degree of complexity because: 1) data are often acquired from sensors of different modalities and with different degrees of uncertainty; 2) decisions must be made efficiently; and 3) the world situation evolves over time. To address these issues, we propose an information fusion framework based on dynamic Bayesian networks to provide active, dynamic, purposive and sufficing information fusion in order to arrive at a reliable conclusion with reasonable time and limited resources. The proposed framework is suited to applications where the decision must be made efficiently from dynamically available information of diverse and disparate sources. Index Terms—Active sensing, Bayesian networks, information fusion. I.

This work surveys the major methods for model-based active sensing in robotics. Active sensing in robotics incorporates the following aspects: (i) where to position sensors, and (ii) how to make decisions for next actions, in order to maximize information gain and minimize costs.

In the past decades most object recognition systems were based on passive approaches. But in the last few years a lot of research was done in the field of active object recognition. In this context there are several unique problems to be solved, like the fusion of several views and the selection of the best next viewpoint.

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Computational models have played a central role in the debate over language learnability. This article discusses how they have been used in different “stances”, from generative views to more recently introduced explanatory frameworks based on embodiment, cognitive development and cultural evolution. By digging into the details of certain specific models, we show how they organize, transform and rephrase defining questions about what makes language learning possible for children. Finally, we present a tentative synthesis to recast the debate using the notion of learning bias.