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A more natural way for non-expert users to express their tasks in an open-ended set is to use natural language. In this case, a human-centered intelligent agent U+002F robot is required to be able to understand and generate plans for these naturally expressed tasks. For this purpose, it is a good way to enhance intelligent robot U+02BC s abilities by utilizing open knowledge extracted from the web,...
In this paper, we propose a novel approach to generate a temporal consistent semantic map for 3D indoor scenes. In contrast to previous techniques which generate the semantic map on the whole global scene immediately or smooth the semantic predictions by a graph model, we intend to discover temporal information over RGB-D images and leverage it to enforce the label consistency. Our method contains...
Intelligent robots require a semantic map of the surroundings for applications such as navigation and object localization. With this information, a robot can make task planning, object manipulation and human-robot interaction. However, it still remains an open problem although considerable emphasis has been given. In this paper, we propose a novel approach to generate a dense semantic map for 3D indoor...
Correctly interpreting human instructions is the first step to human-robot interaction. Previous approaches to semantically parsing the instructions relied on large numbers of training examples with annotation to widely cover all words in a domain. Annotating large enough instructions with semantic forms needs exhaustive engineering efforts. Hence, we propose propagating the semantic lexicon to learn...
For the purpose of smooth human-robot interaction, a robot is supposed to be capable of semantically parsing the human instructions in a large scale. However, the existing supervised approaches to learning a large-scale semantic parser needs a good deal of training examples with annotations. The exhaustive cost of annotating enough sentences prevents them from learning such parser for interpreting...
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