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A novel image-based approach for interactive characterization of rock fracture spacing in a tunnel face
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Categorisation, Typicality & Object-Specific Features in Spatial Referring Expressions
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Modelling the Polysemy of Spatial Prepositions in Referring Expressions
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The Role of Pragmatics in Solving the Winograd Schema Challenge
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Learning of Object Properties, Spatial Relations, and Actions for Embodied Agents from Language and Vision
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Natural Language Grounding and Grammar Induction for Robotic Manipulation Commands
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Abstract:
We present a cognitively plausible system capable of acquiring knowledge in language and vision from pairs of short video clips and linguistic descriptions. The aim of this work is to teach a robot manipulator how to execute natural language commands by demonstration. This is achieved by first learning a set of visual `concepts' that abstract the visual feature spaces into concepts that have human-level meaning. Second, learning the mapping/grounding between words and the extracted visual concepts. Third, inducing grammar rules via a semantic representation known as Robot Control Language (RCL). We evaluate our approach against state-of-the-art supervised and unsupervised grounding and grammar induction systems, and show that a robot can learn to execute never seen-before commands from pairs of unlabelled linguistic and visual inputs.
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URL: http://aclweb.org/anthology/W/W17/W17-28.pdf http://eprints.whiterose.ac.uk/119757/
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Natural Language Acquisition and Grounding for Embodied Robotic Systems
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Grounding language in perception for scene conceptualization in autonomous robots
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Interactive semantic feedback for intuitive ontology authoring
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From Video to RCC8: Exploiting a Distance Based Semantics to Stabilise the Interpretation of Mereotopological Relations
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Online perceptual learning and natural language acquisition for autonomous robots
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