Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation

Modern robotics applications that involve human-robot interaction require robots to be able to communicate with humans seamlessly and effectively. Natural language provides a flexible and efficient medium through which robots can exchange information with their human partners. Significant advancemen...

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Vydáno v:2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI s. 109 - 118
Hlavní autoři: Daniele, Andrea F., Bansal, Mohit, Walter, Matthew R.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: New York, NY, USA ACM 06.03.2017
Edice:ACM Conferences
Témata:
Computing methodologies > Artificial intelligence > Natural language processing > Machine translation
Computing methodologies > Artificial intelligence > Natural language processing > Natural language generation
Computing methodologies > Machine learning > Learning settings > Learning from demonstrations
Computing methodologies > Machine learning > Machine learning approaches > Markov decision processes
Face
Natural languages
Navigation
Robot kinematics
Task analysis
Theory of computation > Theory and algorithms for application domains > Machine learning theory > Reinforcement learning > Inverse reinforcement learning
human-robot interaction
selective generation
natural language generation
ISBN:9781450343367, 1450343368
ISSN:2167-2148
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Shrnutí:Modern robotics applications that involve human-robot interaction require robots to be able to communicate with humans seamlessly and effectively. Natural language provides a flexible and efficient medium through which robots can exchange information with their human partners. Significant advancements have been made in developing robots capable of interpreting free-form instructions, but less attention has been devoted to endowing robots with the ability to generate natural language. We propose a model that enables robots to generate natural language instructions that allow humans to navigate a priori unknown environments. We first decide which information to share with the user according to their preferences, using a policy trained from human demonstrations via inverse reinforcement learning. We then "translate" this information into a natural language instruction using a neural sequence-to-sequence model that learns to generate free-form instructions from natural language corpora. We evaluate our method on a benchmark route instruction dataset and achieve a BLEU score of 72.18% compared to human-generated reference instructions. We additionally conduct navigation experiments with human participants demonstrating that our method generates instructions that people follow as accurately and easily as those produced by humans.
ISBN:9781450343367
1450343368
ISSN:2167-2148
DOI:10.1145/2909824.3020241