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Understanding Natural Language Commands for Robotic Navigation and Mobile Manipulation
TLDR
This paper describes a new model for understanding natural language commands given to autonomous systems that perform navigation and mobile manipulation in semi-structured environments. Expand
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Toward understanding natural language directions
TLDR
We present a system that follows natural language directions by extracting a sequence of spatial description clauses from the linguistic input and then infers the most probable path through the environment given only information about the environmental geometry and detected visible objects. Expand
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Jointly Learning to Parse and Perceive: Connecting Natural Language to the Physical World
TLDR
This paper introduces Logical Semantics with Perception (LSP), a model for grounded language acquisition that learns to map natural language statements to their referents in a physical environment. Expand
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Utilizing object-object and object-scene context when planning to find things
  • T. Kollar, N. Roy
  • Computer Science, Engineering
  • IEEE International Conference on Robotics and…
  • 12 May 2009
TLDR
In this paper, our goal is to search for a novel object, where we have a prior map of the environment and knowledge of some of the objects in it, but no information about the location of the novel object. Expand
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Trajectory Optimization using Reinforcement Learning for Map Exploration
TLDR
In this paper, we address the problem of how a robot should plan to explore an unknown environment and collect data in order to maximize the accuracy of the resulting map. Expand
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Topological mapping using spectral clustering and classification
TLDR
In this work we present an online method for generating topological maps from raw sensor information. Expand
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Indoor scene recognition through object detection
TLDR
We propose a new approach for indoor scene recognition based on a generative probabilistic hierarchical model that uses common objects as an intermediate semantic representation. Expand
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Clarifying commands with information-theoretic human-robot dialog
TLDR
We present an approach for enabling a robot to engage in clarifying dialog with a human partner, just as a human might do in a similar situation. Expand
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Approaching the Symbol Grounding Problem with Probabilistic Graphical Models
TLDR
We use probabilistic inference to address the symbol grounding problem to develop models that factor according to the linguistic structure of a command. Expand
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Toward understanding natural language directions
TLDR
We present a system that follows natural language directions by extracting a sequence of spatial description clauses from the linguistic input and then infers the most probable path through the environment given only information about the environmental geometry and detected visible objects. Expand
  • 109
  • 4
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