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
On-line přístup:	Získat plný text
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