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We present an integrated vision and robotic system that plays, and learns to play, simple physically-instantiated board games that are variants of TIC TAC TOE and HEXA-PAWN. We employ novel custom vision and robotic hardware designed specifically for this learning task. The game rules can be parametrically specified. Two independent computational agents alternate playing the two opponents with the...
We aim to perform robust and fast vision-based localization using a pre-existing large map of the scene. A key step in localization is associating the features extracted from the image with the map elements at the current location. Although the problem of data association has greatly benefited from recent advances in appearance-based matching methods, less attention has been paid to the effective...
Our goal is that the robot learns specific objects (not object category) from images. The major problem here is how to separate the target object from the background. We create a scene model from an image sequence. The scene model contains both the target object and background. We separate the target object from the background by matching the scene model and training images having different backgrounds...
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