Extending Referring Expression Generation through shared knowledge about past Human-Robot collaborative activity

Guillaume Sarthou; Guilhem Buisan; Aurélie Clodic; Rachid Alami

IROS 2021poster3 citations

Extending Referring Expression Generation through shared knowledge about past Human-Robot collaborative activity

Guillaume Sarthou, Guilhem Buisan, Aurélie Clodic, Rachid Alami

Abstract

Being able to refer to an object, a person, or a place in a non-ambiguous manner is a need when one has to achieve collaborative activities with a partner. This is the so-called Referring Expression Generation (REG) problem. While widely used for Human-Robot Interaction, state of the art approaches restrict its use to the current environment. We propose a novel extension to the REG which takes full advantage of the Human-Robot shared knowledge about past actions as additional information to generate Referring Expressions. We show that our approach is usable with a domain-independent ontology as a knowledge base and that it can also use a semantic representation of past activity to generate RE. We illustrate our method through simulated situations and discuss its efficiency and pertinence.

BibTeX

@inproceedings{iros2021_extendingreferri,
  title = {Extending Referring Expression Generation through shared knowledge about past Human-Robot collaborative activity},
  author = {Guillaume Sarthou and Guilhem Buisan and Aurélie Clodic and Rachid Alami},
  booktitle = {IROS 2021},
  year = {2021}
}