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Learning Semantic Maps from Natural Language Descriptions
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@MISC{Walter_learningsemantic,
author = {Matthew R. Walter and Sachithra Hemach and Bianca Homberg and Stefanie Tellex and Seth Teller},
title = {Learning Semantic Maps from Natural Language Descriptions},
year = {}
}
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Abstract
Abstract—This paper proposes an algorithm that enables robots to efficiently learn human-centric models of their environment from natural language descriptions. Typical semantic mapping approaches augment metric maps with higher-level properties of the robot’s surroundings (e.g., place type, object locations), but do not use this information to improve the metric map. The novelty of our algorithm lies in fusing high-level knowledge, conveyed by speech, with metric information from the robot’s low-level sensor streams. Our method jointly estimates a hybrid metric, topological, and semantic representation of the environment. This semantic graph provides a common framework in which we integrate concepts from natural language descriptions (e.g., labels and spatial relations) with metric observations from low-level sensors. Our algorithm efficiently maintains a factored distribution over semantic graphs based upon the stream of natural language and low-level sensor information. We evaluate the algorithm’s performance and demonstrate that the incorporation of information from natural language increases the metric, topological and semantic accuracy of the recovered environment model. The kitchen is down the hall Fig. 1. A user giving a tour to a robotic wheelchair designed to assist residents in a long-term care facility. I.
Keyphrases
natural language description semantic map semantic graph natural language higher-level property robot surroundings metric information metric observation high-level knowledge spatial relation low-level sensor stream robotic wheelchair recovered environment model object location place type hall fig semantic representation long-term care facility low-level sensor information semantic accuracy low-level sensor factored distribution algorithm performance human-centric model metric map common framework
