@inproceedings{kane-etal-2022-system,
title = "A System For Robot Concept Learning Through Situated Dialogue",
author = "Kane, Benjamin and
Gervits, Felix and
Scheutz, Matthias and
Marge, Matthew",
editor = "Lemon, Oliver and
Hakkani-Tur, Dilek and
Li, Junyi Jessy and
Ashrafzadeh, Arash and
Garcia, Daniel Hern{\'a}ndez and
Alikhani, Malihe and
Vandyke, David and
Du{\v{s}}ek, Ond{\v{r}}ej",
booktitle = "Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2022",
address = "Edinburgh, UK",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.sigdial-1.64",
doi = "10.18653/v1/2022.sigdial-1.64",
pages = "659--662",
abstract = "Robots operating in unexplored environments with human teammates will need to learn unknown concepts on the fly. To this end, we demonstrate a novel system that combines a computational model of question generation with a cognitive robotic architecture. The model supports dynamic production of back-and-forth dialogue for concept learning given observations of an environment, while the architecture supports symbolic reasoning, action representation, one-shot learning and other capabilities for situated interaction. The system is able to learn about new concepts including objects, locations, and actions, using an underlying approach that is generalizable and scalable. We evaluate the system by comparing learning efficiency to a human baseline in a collaborative reference resolution task and show that the system is effective and efficient in learning new concepts, and that it can informatively generate explanations about its behavior.",
}
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<abstract>Robots operating in unexplored environments with human teammates will need to learn unknown concepts on the fly. To this end, we demonstrate a novel system that combines a computational model of question generation with a cognitive robotic architecture. The model supports dynamic production of back-and-forth dialogue for concept learning given observations of an environment, while the architecture supports symbolic reasoning, action representation, one-shot learning and other capabilities for situated interaction. The system is able to learn about new concepts including objects, locations, and actions, using an underlying approach that is generalizable and scalable. We evaluate the system by comparing learning efficiency to a human baseline in a collaborative reference resolution task and show that the system is effective and efficient in learning new concepts, and that it can informatively generate explanations about its behavior.</abstract>
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%0 Conference Proceedings
%T A System For Robot Concept Learning Through Situated Dialogue
%A Kane, Benjamin
%A Gervits, Felix
%A Scheutz, Matthias
%A Marge, Matthew
%Y Lemon, Oliver
%Y Hakkani-Tur, Dilek
%Y Li, Junyi Jessy
%Y Ashrafzadeh, Arash
%Y Garcia, Daniel Hernández
%Y Alikhani, Malihe
%Y Vandyke, David
%Y Dušek, Ondřej
%S Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2022
%8 September
%I Association for Computational Linguistics
%C Edinburgh, UK
%F kane-etal-2022-system
%X Robots operating in unexplored environments with human teammates will need to learn unknown concepts on the fly. To this end, we demonstrate a novel system that combines a computational model of question generation with a cognitive robotic architecture. The model supports dynamic production of back-and-forth dialogue for concept learning given observations of an environment, while the architecture supports symbolic reasoning, action representation, one-shot learning and other capabilities for situated interaction. The system is able to learn about new concepts including objects, locations, and actions, using an underlying approach that is generalizable and scalable. We evaluate the system by comparing learning efficiency to a human baseline in a collaborative reference resolution task and show that the system is effective and efficient in learning new concepts, and that it can informatively generate explanations about its behavior.
%R 10.18653/v1/2022.sigdial-1.64
%U https://aclanthology.org/2022.sigdial-1.64
%U https://doi.org/10.18653/v1/2022.sigdial-1.64
%P 659-662
Markdown (Informal)
[A System For Robot Concept Learning Through Situated Dialogue](https://aclanthology.org/2022.sigdial-1.64) (Kane et al., SIGDIAL 2022)
ACL
- Benjamin Kane, Felix Gervits, Matthias Scheutz, and Matthew Marge. 2022. A System For Robot Concept Learning Through Situated Dialogue. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 659–662, Edinburgh, UK. Association for Computational Linguistics.