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Predicting the sensory consequences of an agent's own actions is considered an important skill for intelligent behavior. In terms of vision, so-called visual forward models can be applied to learn such predictions. This is no trivial task given the high-dimensionality of sensory data and complex action spaces. In this work, we propose to learn the visual consequences of changes in pan and tilt of...
A common characteristics of the computational models of visual attention is they execute the two modes of visual attention (visual exploration and visual search) separately. This makes a visual attention model unsuitable for real-world robotic applications. This paper focuses on integrating visual exploration and visual search in a common framework of visual attention and the challenges resulting...
For a developmental robotic system to function successfully in the real world, it is important that it be able to form its own internal representations of affordance classes based on observable regularities in sensory data. Usually successful classifiers are built using labeled training data, but it is not always realistic to assume that labels are available in a developmental robotics setting. There...
This paper describes an integrated robot system, known as Curious George, that has demonstrated state-of-the-art capabilities to recognize objects in the real world. We describe the capabilities of this system, including: the ability to access web-based training data automatically and in near real-time, the ability to model the visual appearance and 3D shape of a wide variety of object categories,...
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