Causal Learning for Human-Robot Interaction 2024

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. However, much of this progress is grounded in pattern recognition and statistical correlation-based machine learning (ML), neglecting the intrinsic structures and interdependencies between variables in observational data and the underlying causal relationships that govern the emergence of these dependencies. Causality precisely focuses on unraveling such causal structures and relationships inherent in the data. Many challenges within ML and HRI, including generalisation and bias issues, can be attributed to this ignorance of cause-and-effect relationships between data variables. 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 can 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.