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This paper show how neural networks, configured for regression, can be used to learn the relationships between Inertial Motion Unit (IMU) data collected on a robotic platform and the robot's commanded system state. By learning how the IMU data relates to commanded robot state we can use the neural network to predict what commands have been issued to the robot. By comparing the prediction to the actual...
A common approach to the robotic path planning problem is to discretize the search space and plan an optimal path using graph search methods. As the optimality criteria becomes more complex it becomes increasingly difficult to optimize the search with respect to each metric being considered. A hybrid path planning system is introduced that supplements the graph search approach with a cognitive architecture...
While remotely operated unmanned vehicles are increasingly a part of everyday life, truly autonomous robots capable of independent operation in dynamic environments have yet to be realized — particularly in the case of ground robots required to interact with humans and their environment. We present a unified multiresolution vision model for this application designed to provide the wide field of view...
The Human Visual System (HVS) exhibits multi-resolution characteristics, where the fovea is at the highest resolution while the resolution tapers off towards the periphery. Given enough activity at the periphery, the HVS is then capable to foveate to the next region of interest (ROI), to attend to it at full resolution. Saliency models in the past have focused on identifying features that can be used...
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