HRI 2024 Workshop on Causal Learning for Human-Robot Interaction (Causal-HRI)

• Website: https://causal-hri.github.io 
• Workshop: 13:00-17:00 MST on March 11, 2024
• Location: Hybrid (Boulder, Colorado and online), as part of the 19^th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2024)
• Manuscript submission site: https://easychair.org/conferences/?conf=causalhri2024 
• Contact for submissions: lguerdan [AT] cs.cmu.edu

Important Dates

• Paper Submission Deadline: 02 February, 2024   09 February, 2024
• Notification of Acceptance (Papers): 16 February, 2024
• Poster Submission Deadline: 16 February, 2024
• Notification of Acceptance (Posters): 23 February, 2024
• Camera-ready Deadline: 01 March, 2024
• Workshop Day: 11 March, 2024

All deadlines are at 23:59 Anywhere on Earth time.

Aim and Scope

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. 

Join the workshop Slack Channel to stay updated and connect with the rest of the community.

List of Topics

The workshop welcomes contributions across a wide range of topics including, but not limited to:

• Causal inference
• Counterfactual reasoning for robotics
• Causal representation learning
• Causal Learning for Skill-discovery for Robots
• Causal discovery of latent graphs for robotic behavioural learning
• Explanations for Robot Behaviours
• Generalised representation learning for Human-Robot Interaction
• Scene understanding with Causal Inference
• Causal Learning for Human behaviour understanding
• Causal Learning for State/Action-space inferences
• Explainable Human-Robot Interaction
• Applications for Causal HRI
• Research datasets, software, open-source tools, hardware analysis, system benchmarks in/for Causal HRI

Workshop Schedule

The workshop will be hybrid (in Boulder, Colorado and online) on March 11, 2024, from 09:00 to 13:00 MT. It will consist of three keynote talks (30-minute presentation, 10-minute Q&A), accepted paper talks (8-minute presentation, 2-minute Q&A), poster session / lightning talks (30 minutes) and a group discussion session (20 minutes). The website has a detailed schedule.

Keynotes

• Prof. Holly Yanco, University of Massachusetts Lowell (USA)
• Prof. Alison Gopnik, University of California at Berkeley (USA)
• Prof. Karinne Ramirez-Amaro, Chalmers University of Technology (Sweden)

Submission Guidelines

We invite authors to submit their contributions as 3-4 page papers (plus additional pages for references and appendices), highlighting their experimental results, technical reports, and case studies discussing Causal Learning for Human-Robot Interaction. In particular, we will encourage submissions addressing the theme of HRI 2024: "HRI in the real-world." All submissions will be peer-reviewed for their novelty, relevance, contribution to the field, and technical soundness.

We also invite researchers to submit position articles as 1-2 page extended abstracts for poster presentations / lightning talks. These abstracts will receive a light review that examines fit and factual correctness, but the authors will receive feedback from the audience during the workshop.

Similar to the main HRI conference, all submissions should be made following the general ACM SIG format (“sigconf”, double column format) and not the SIGCHI format. Templates can be found here or on Overleaf directly.

Organisers

• Jiaee Cheong, PhD Student, University of Cambridge (UK).
• Nikhil Churamani, Postdoctoral Researcher, University of Cambridge (UK).  
• Luke Guerdan, PhD Student, Carnegie Mellon University (USA).
• Tabitha Edith Lee, PhD Student, Carnegie Mellon University (USA).
• Zhao Han, Assistant Professor, University of South Florida (USA).
• Hatice Gunes, Professor, University of Cambridge (UK).  

Contact

All questions about submissions should be emailed to lguerdan [AT] cs.cmu.edu.