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This paper presents an equivalence between feasible kinodynamic planning and optimal kinodynamic planning, in that any optimal planning problem can be transformed into a series of feasible planning problems in a state-cost space, whose solutions approach the optimum. This transformation yields a meta-algorithm that produces an asymptotically optimal(More)
To enhance the experience of a home network user, priori-tization of network traffic is important. Previous solutions to this problem assigned priorities in a static manner. Even though there has been some efforts to assign priorities dynamically , these solutions only used interactivity of applications to prioritize traffic. We present Contextual Router,(More)
—We present a framework for representing scenarios with complex object interactions, in which a robot cannot directly interact with the object it wishes to control, but must instead do so via intermediate objects. For example, a robot learning to drive a car can only indirectly change its pose, by rotating the steering wheel. We formalize such complex(More)
— Convolutional neural networks (CNN) are a deep learning technique that has achieved state-of-the-art prediction performance in computer vision and robotics, but assume the input data can be formatted as an image or video (e.g. predicting a robot grasping location given RGB-D image input). This paper considers the problem of augmenting a traditional CNN(More)
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