A Joint Model of Language and Perception for Grounded Attribute Learning
Links to Fileshttps://arxiv.org/abs/1206.6423
MetadataShow full item record
Type of Work8 pages
conference papers and proceedings preprints
Citation of Original PublicationCynthia Matuszek, et.al, A Joint Model of Language and Perception for Grounded Attribute Learning, 29th International Conference on Machine Learning (ICML 2012), https://arxiv.org/abs/1206.6423
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Subjectsgrounded attribute learning
probabilistic categorial grammar
latent-variable concept induction in physical grounded scenes
Interactive Robotics and Language Lab
As robots become more ubiquitous and capable, it becomes ever more important to enable untrained users to easily interact with them. Recently, this has led to study of the language grounding problem, where the goal is to extract representations of the meanings of natural language tied to perception and actuation in the physical world. In this paper, we present an approach for joint learning of language and perception models for grounded attribute induction. Our perception model includes attribute classifiers, for example to detect object color and shape, and the language model is based on a probabilistic categorial grammar that enables the construction of rich, compositional meaning representations. The approach is evaluated on the task of interpreting sentences that describe sets of objects in a physical workspace. We demonstrate accurate task performance and effective latent-variable concept induction in physical grounded scenes.