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Communication Dans Un Congrès Année : 2021

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

Guillaume Sarthou
Guilhem Buisan
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Aurélie Clodic
Rachid Alami

Résumé

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 socalled 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.
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Dates et versions

hal-03389504 , version 1 (21-10-2021)

Identifiants

Citer

Guillaume Sarthou, Guilhem Buisan, Aurélie Clodic, Rachid Alami. Extending Referring Expression Generation through shared knowledge about past Human-Robot collaborative activity. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Sep 2021, Prague (online), Czech Republic. ⟨10.1109/IROS51168.2021.9636796⟩. ⟨hal-03389504⟩
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