Learning representations for robust human–robot interaction

Abstract

This article summarizes the author's presentation in the New Faculty Highlight at the Thirty-Eighth AAAI Conference on Artificial Intelligence. It discusses the desired properties of representations for enabling robust human–robot interaction. Examples from the author's work are presented to show how to build these properties into models for performing tasks with natural language guidance and engaging in social interactions with other agents.