Grounding Language for Interactive Task Learning

Peter Lindes, Aaron Mininger, James R. Kirk, John E. Laird


Abstract
This paper describes how language is grounded by a comprehension system called Lucia within a robotic agent called Rosie that can manipulate objects and navigate indoors. The whole system is built within the Soar cognitive architecture and uses Embodied Construction Grammar (ECG) as a formalism for describing linguistic knowledge. Grounding is performed using knowledge from the grammar itself, from the linguistic context, from the agents perception, and from an ontology of long-term knowledge about object categories and properties and actions the agent can perform. The paper also describes a benchmark corpus of 200 sentences in this domain along with test versions of the world model and ontology and gold-standard meanings for each of the sentences. The benchmark is contained in the supplemental materials.
Anthology ID:
W17-2801
Volume:
Proceedings of the First Workshop on Language Grounding for Robotics
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
RoboNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–9
Language:
URL:
https://aclanthology.org/W17-2801
DOI:
10.18653/v1/W17-2801
Bibkey:
Cite (ACL):
Peter Lindes, Aaron Mininger, James R. Kirk, and John E. Laird. 2017. Grounding Language for Interactive Task Learning. In Proceedings of the First Workshop on Language Grounding for Robotics, pages 1–9, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Grounding Language for Interactive Task Learning (Lindes et al., RoboNLP 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/W17-2801.pdf
Dataset:
 W17-2801.Datasets.zip
Poster:
 W17-2801.Poster.pdf