@inproceedings{bonial-etal-2023-abstract,
title = "{A}bstract {M}eaning {R}epresentation for Grounded Human-Robot Communication",
author = "Bonial, Claire and
Foresta, Julie and
Fung, Nicholas C. and
Hayes, Cory J. and
Osteen, Philip and
Arkin, Jacob and
Hedegaard, Benned and
Howard, Thomas",
editor = "Bonn, Julia and
Xue, Nianwen",
booktitle = "Proceedings of the Fourth International Workshop on Designing Meaning Representations",
month = jun,
year = "2023",
address = "Nancy, France",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.dmr-1.4",
pages = "34--44",
abstract = "To collaborate effectively in physically situated tasks, robots must be able to ground concepts in natural language to the physical objects in the environment as well as their own capabilities. We describe the implementation and the demonstration of a system architecture that sup- ports tasking robots using natural language. In this architecture, natural language instructions are first handled by a dialogue management component, which provides feedback to the user and passes executable instructions along to an Abstract Meaning Representation (AMR) parser. The parse distills the action primitives and parameters of the instructed behavior in the form of a directed a-cyclic graph, passed on to the grounding component. We find AMR to be an efficient formalism for grounding the nodes of the graph using a Distributed Correspondence Graph. Thus, in our approach, the concepts of language are grounded to entities in the robot{'}s world model, which is populated by its sensors, thereby enabling grounded natural language communication. The demonstration of this system will allow users to issue navigation commands in natural language to direct a simulated ground robot (running the Robot Operating System) to various landmarks observed by the user within a simulated environment.",
}
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<abstract>To collaborate effectively in physically situated tasks, robots must be able to ground concepts in natural language to the physical objects in the environment as well as their own capabilities. We describe the implementation and the demonstration of a system architecture that sup- ports tasking robots using natural language. In this architecture, natural language instructions are first handled by a dialogue management component, which provides feedback to the user and passes executable instructions along to an Abstract Meaning Representation (AMR) parser. The parse distills the action primitives and parameters of the instructed behavior in the form of a directed a-cyclic graph, passed on to the grounding component. We find AMR to be an efficient formalism for grounding the nodes of the graph using a Distributed Correspondence Graph. Thus, in our approach, the concepts of language are grounded to entities in the robot’s world model, which is populated by its sensors, thereby enabling grounded natural language communication. The demonstration of this system will allow users to issue navigation commands in natural language to direct a simulated ground robot (running the Robot Operating System) to various landmarks observed by the user within a simulated environment.</abstract>
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%0 Conference Proceedings
%T Abstract Meaning Representation for Grounded Human-Robot Communication
%A Bonial, Claire
%A Foresta, Julie
%A Fung, Nicholas C.
%A Hayes, Cory J.
%A Osteen, Philip
%A Arkin, Jacob
%A Hedegaard, Benned
%A Howard, Thomas
%Y Bonn, Julia
%Y Xue, Nianwen
%S Proceedings of the Fourth International Workshop on Designing Meaning Representations
%D 2023
%8 June
%I Association for Computational Linguistics
%C Nancy, France
%F bonial-etal-2023-abstract
%X To collaborate effectively in physically situated tasks, robots must be able to ground concepts in natural language to the physical objects in the environment as well as their own capabilities. We describe the implementation and the demonstration of a system architecture that sup- ports tasking robots using natural language. In this architecture, natural language instructions are first handled by a dialogue management component, which provides feedback to the user and passes executable instructions along to an Abstract Meaning Representation (AMR) parser. The parse distills the action primitives and parameters of the instructed behavior in the form of a directed a-cyclic graph, passed on to the grounding component. We find AMR to be an efficient formalism for grounding the nodes of the graph using a Distributed Correspondence Graph. Thus, in our approach, the concepts of language are grounded to entities in the robot’s world model, which is populated by its sensors, thereby enabling grounded natural language communication. The demonstration of this system will allow users to issue navigation commands in natural language to direct a simulated ground robot (running the Robot Operating System) to various landmarks observed by the user within a simulated environment.
%U https://aclanthology.org/2023.dmr-1.4
%P 34-44
Markdown (Informal)
[Abstract Meaning Representation for Grounded Human-Robot Communication](https://aclanthology.org/2023.dmr-1.4) (Bonial et al., DMR-WS 2023)
ACL
- Claire Bonial, Julie Foresta, Nicholas C. Fung, Cory J. Hayes, Philip Osteen, Jacob Arkin, Benned Hedegaard, and Thomas Howard. 2023. Abstract Meaning Representation for Grounded Human-Robot Communication. In Proceedings of the Fourth International Workshop on Designing Meaning Representations, pages 34–44, Nancy, France. Association for Computational Linguistics.