@inproceedings{alomari-etal-2017-natural,
title = "Natural Language Grounding and Grammar Induction for Robotic Manipulation Commands",
author = "Alomari, Muhannad and
Duckworth, Paul and
Hawasly, Majd and
Hogg, David C. and
Cohn, Anthony G.",
editor = "Bansal, Mohit and
Matuszek, Cynthia and
Andreas, Jacob and
Artzi, Yoav and
Bisk, Yonatan",
booktitle = "Proceedings of the First Workshop on Language Grounding for Robotics",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2805/",
doi = "10.18653/v1/W17-2805",
pages = "35--43",
abstract = "We present a cognitively plausible system capable of acquiring knowledge in language and vision from pairs of short video clips and linguistic descriptions. The aim of this work is to teach a robot manipulator how to execute natural language commands by demonstration. This is achieved by first learning a set of visual `concepts' that abstract the visual feature spaces into concepts that have human-level meaning. Second, learning the mapping/grounding between words and the extracted visual concepts. Third, inducing grammar rules via a semantic representation known as Robot Control Language (RCL). We evaluate our approach against state-of-the-art supervised and unsupervised grounding and grammar induction systems, and show that a robot can learn to execute never seen-before commands from pairs of unlabelled linguistic and visual inputs."
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%0 Conference Proceedings
%T Natural Language Grounding and Grammar Induction for Robotic Manipulation Commands
%A Alomari, Muhannad
%A Duckworth, Paul
%A Hawasly, Majd
%A Hogg, David C.
%A Cohn, Anthony G.
%Y Bansal, Mohit
%Y Matuszek, Cynthia
%Y Andreas, Jacob
%Y Artzi, Yoav
%Y Bisk, Yonatan
%S Proceedings of the First Workshop on Language Grounding for Robotics
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F alomari-etal-2017-natural
%X We present a cognitively plausible system capable of acquiring knowledge in language and vision from pairs of short video clips and linguistic descriptions. The aim of this work is to teach a robot manipulator how to execute natural language commands by demonstration. This is achieved by first learning a set of visual ‘concepts’ that abstract the visual feature spaces into concepts that have human-level meaning. Second, learning the mapping/grounding between words and the extracted visual concepts. Third, inducing grammar rules via a semantic representation known as Robot Control Language (RCL). We evaluate our approach against state-of-the-art supervised and unsupervised grounding and grammar induction systems, and show that a robot can learn to execute never seen-before commands from pairs of unlabelled linguistic and visual inputs.
%R 10.18653/v1/W17-2805
%U https://aclanthology.org/W17-2805/
%U https://doi.org/10.18653/v1/W17-2805
%P 35-43
Markdown (Informal)
[Natural Language Grounding and Grammar Induction for Robotic Manipulation Commands](https://aclanthology.org/W17-2805/) (Alomari et al., RoboNLP 2017)
ACL