Learning spatial-semantic representations from natural language descriptions and scene classifications

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Grounded Language Learning in a Simulated 3D World (2017, background): “Within robotics, there has been interest in using language to facilitate human-robot communication, as part of which it is necessary to devise mechanisms for grounding a perceptible environment with language (Hemachandra et al., 2014; Walter et al., 2014).”
Deep Transfer in Reinforcement Learning by Language Grounding (2017, background): “Another related line of work consists of systems that learn spatial and topographical maps of the environment for robot navigation using natural language descriptions (Walter et al., 2013; Hemachandra et al., 2014).”
SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities (2024, methodology): “When applying these spatial concepts to reasoning, prior works often focus on explicit spatial scene memories [26, 27] or spatial scene graphs [29, 30, 59, 60].”
Learning models for following natural language directions in unknown environments (2015, methodology): “directions [2], [3], [4], [5], that command manipulation [6], [7], and that convey environment knowledge [8], [9].”
A framework for learning semantic maps from grounded natural language descriptions (2014, methodology): “sophisticated segmentation strategy that takes into account local appearance (Brunskill et al., 2007; Ranganathan and Dellaert, 2009; Pronobis and Jensfelt, 2012; Hemachandra et al., 2014) and semantic (Martínez Mozos et al.”