Causal-HRI: Causal Learning for Human-Robot Interaction

Jiaee Cheong, Nikhil Churamani, Luke Guerdan, Tabitha Edith Lee, Zhao Han, Hatice Gunes

Abstract: Real-world Human-Robot Interaction (HRI) requires robots to adeptly perceive and understand the dynamic human-centred environments in which they operate. Recent decades have seen remarkable advancements that have endowed robots with exceptional perception capabilities. The first workshop on "Causal-HRI: Causal Learning for Human-Robot Interaction" aims to bring together research perspectives from Causal Discovery and Inference and Causal Learning, in general, to real-world HRI applications. The objective of this workshop is to explore strategies that will not only embed robots with capabilities to discover cause-and-effect relationships from observations, allowing them to generalise to unseen interaction settings, but also to enable users to understand robot behaviours, moving beyond the 'black-box' models used by these robots. This workshop aims to facilitate an exchange of views through invited keynote presentations, contributed talks, group discussions and poster sessions, encouraging collaborations across diverse scientific communities. The theme of HRI 2024, "HRI in the real world," will inform the overarching theme of this workshop, encouraging discussions on HRI theories, methods, designs and studies focused on leveraging Causal Learning for enhancing real-world HRI.