Extending Referring Expression Generation through shared knowledge about past Human-Robot collaborative activity
Guillaume Sarthou, Guilhem Buisan, Aurélie Clodic, Rachid Alami · 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · 2021
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.