Symbol and Communicative Grounding through Object Permanence with a Mobile Robot

Josue Torres-Fonseca, Catherine Henry, Casey Kennington


Abstract
Object permanence is the ability to form and recall mental representations of objects even when they are not in view. Despite being a crucial developmental step for children, object permanence has had only some exploration as it relates to symbol and communicative grounding in spoken dialogue systems. In this paper, we leverage SLAM as a module for tracking object permanence and use a robot platform to move around a scene where it discovers objects and learns how they are denoted. We evaluated by comparing our system’s effectiveness at learning words from human dialogue partners both with and without object permanence. We found that with object permanence, human dialogue partners spoke with the robot and the robot correctly identified objects it had learned about significantly more than without object permanence, which suggests that object permanence helped facilitate communicative and symbol grounding.
Anthology ID:
2022.sigdial-1.14
Volume:
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
Month:
September
Year:
2022
Address:
Edinburgh, UK
Editors:
Oliver Lemon, Dilek Hakkani-Tur, Junyi Jessy Li, Arash Ashrafzadeh, Daniel Hernández Garcia, Malihe Alikhani, David Vandyke, Ondřej Dušek
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
124–134
Language:
URL:
https://aclanthology.org/2022.sigdial-1.14
DOI:
10.18653/v1/2022.sigdial-1.14
Bibkey:
Cite (ACL):
Josue Torres-Fonseca, Catherine Henry, and Casey Kennington. 2022. Symbol and Communicative Grounding through Object Permanence with a Mobile Robot. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 124–134, Edinburgh, UK. Association for Computational Linguistics.
Cite (Informal):
Symbol and Communicative Grounding through Object Permanence with a Mobile Robot (Torres-Fonseca et al., SIGDIAL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.sigdial-1.14.pdf
Video:
 https://youtu.be/xxulenUa754
Data
MS COCO