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We introduce a novel active stereo vison-based object tracking system for a humanoid robot. The system tracks a moving object that is dynamically changing its appearance and scale. The system features an in-built learning process that simultaneously learns short term models for the object and potential distractors. These models evolve over time, rectifying the inaccuracies of the tracking in a cluttered...
We present a method for autonomously learning representations of visual disparity between images from left and right eye, as well as appropriate vergence movements to fixate objects with both eyes. A sparse coding model (perception) encodes sensory information using binocular basis functions, while a reinforcement learner (behavior) generates the eye movement, according to the sensed disparity. Perception...
Building artificial agents and robots that can act in an intelligent way is one of the main research goals in artificial intelligence and robotics. Yet it is still hard to integrate functional cognitive processes into these systems. We present a framework combining computer vision and machine learning for the learning of object recognition in humanoid robots. A biologically inspired, bottom-up architecture...
In this work we introduce a technique for a humanoid robot to autonomously learn the representations of objects within its visual environment. Our approach involves an attention mechanism in association with feature based segmentation that explores the environment and provides object samples for training. These samples are learned for further object identification using Cartesian Genetic Programming...
We propose a new theory explaining the familiarity-to-novelty shift in infant habituation. In our account, infants' interest in a stimulus is related to their learning progress, i.e. the improvement of an internal model of the stimulus. Specifically, we propose infants prefer the stimulus for which its current learning progress is maximal. We also propose a new algorithm called Selective Learning...
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