Natural Language Direction Following for Robots in Unstructured Unknown Environments

@inproceedings{Duvallet2015NaturalLD,
  title={Natural Language Direction Following for Robots in Unstructured Unknown Environments},
  author={Felix Duvallet},
  year={2015}
}
Abstract : Robots are increasingly performing collaborative tasks with people in homes, workplaces, and outdoors, and with this increase in interaction comes a need for e cient communication between human and robot teammates. One way to achieve this communication is through natural language, which provides a exible and intuitive way to issue commands to robots without requiring specialized interfaces or extensive user training. One task where natural language understanding could facilitate… 
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References

SHOWING 1-10 OF 186 REFERENCES
Imitation learning for natural language direction following through unknown environments
TLDR
This work learns a policy which predicts a sequence of actions that follow the directions by exploring the environment and discovering landmarks, backtracking when necessary, and explicitly declaring when it has reached the destination.
Inferring Maps and Behaviors from Natural Language Instructions
TLDR
This paper proposes a probabilistic framework that enables robots to follow commands given in natural language, without any prior knowledge of the environment, and demonstrates the algorithm’s ability to follow navigation commands with performance comparable to that of a fully-known environment.
Learning models for following natural language directions in unknown environments
TLDR
A novel learning framework is proposed that enables robots to successfully follow natural language route directions without any previous knowledge of the environment by learning and performing inference over a latent environment model.
Toward Mobile Robots Reasoning Like Humans
TLDR
An intelligence architecture that combines cognitive components to carry out high-level cognitive tasks, semantic perception to label regions in the world, and a natural language component to reason about the command and its relationship to the objects in theworld is developed.
Toward Information Theoretic Human-Robot Dialog
TLDR
To enable a robot to recover from a failure to understand a natural language utterance, an information-theoretic strategy for asking targeted clarifying questions and using information from the answer to disambiguate the language is described.
Natural language command of an autonomous micro-air vehicle
TLDR
This paper presents a micro-air vehicle capable of following natural language directions through a previously mapped and labeled environment, and demonstrates the robot following directions created by a human for another human, and interactively executing commands in the context of surveillance and search and rescue in confined spaces.
Interactive robot task training through dialog and demonstration
TLDR
A framework for interactive task training of a mobile robot where the robot learns how to do various tasks while observing a human and how environmental context and communicative dialog with the human help the robot learn the task.
Learning to Parse Natural Language Commands to a Robot Control System
TLDR
This work discusses the problem of parsing natural language commands to actions and control structures that can be readily implemented in a robot execution system, and learns a parser based on example pairs of English commands and corresponding control language expressions.
Learning to understand spatial language for robotic navigation and mobile manipulation
TLDR
A method that uses previously detected objects and places in order to bias the search process toward areas of the environment where a previously unseen object is likely to be found and created on the fly as the robot explores the environment is presented.
A natural language planner interface for mobile manipulators
TLDR
This paper presents a new model called the Distributed Correspondence Graph (DCG) to infer the most likely set of planning constraints from natural language instructions, and presents experimental results from comparative experiments that demonstrate improvements in efficiency in natural language understanding without loss of accuracy.
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