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The goal of this paper is to develop a robot with a grounded spatial vocabulary. Such a vocabulary would allow it to give and follow directions, and would give it valuable additional information in aiding localization and navigation. We approach the problem by defining an ontology of space (including corridor, doorway, and room) and by creating a Convolutional Neural Network (CNN) that allows the...
Many autonomous vehicles require precise localization into a prior map in order to support planning and to leverage semantic information within those maps (e.g. that the right lane is a turn-only lane.) A popular approach in automotive systems is to use infrared intensity maps of the ground surface to localize, making them susceptible to failures when the surface is obscured by snow or when the road...
On-the-fly learning systems are necessary for the deployment of general purpose robots. New training examples for such systems are often supplied by mentor interactions. Due to the cost of acquiring such examples, it is desirable to reduce the number of necessary interactions. Transfer learning has been shown to improve classification results for classes with small numbers of training examples by...
Despite evidence that human wayfinders consider directions involving landmarks or topological descriptions easier to follow, the majority of commerical direction-planning services and GPS navigation units plan routes based on metrically or temporally shortest paths, ignoring this potentially valuable information. We propose a method for generating directions that maximizes the probability of a human...
Tasks like search‐and‐rescue and urban reconnaissance benefit from large numbers of robots working together, but high levels of autonomy are needed to reduce operator requirements to practical levels. Reducing the reliance of such systems on human operators presents a number of technical challenges, including automatic task allocation, global state and map estimation, robot perception, path planning,...
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