IROS 2023 Workshop

 Causality for Robotics: Answering the Question of Why 

Huntington Place, Room 331A/B/C, Detroit, Michigan, October 5th, 2023 


The grand vision of robotics for general-purpose machines that act, perceive, and learn in real-world environments must be met with intelligent capabilities of equal measure. To date, tremendous advances in robotics and artificial intelligence have been achieved primarily based on correlation. Yet, correlation is not causation: correlation-based methods often lack robust generalization, are brittle to distribution shifts, and can learn incorrect, spurious relationships in observed data. In contrast, a hallmark of human intelligence — reasoning about cause and effect — can provide clues for the next generation of robot intelligence. The advantages of adopting the principles of causality have been witnessed in fields such as biomedical science, economics, and genomics. Recently, it has been argued that the machine learning community should adopt the principles of causal inference towards causal learning of representations. Analogously, this workshop argues that the field of robotics stands to gain by integrating causality, moving robotics towards human-like embodied intelligence. Can robots learn and leverage the causal structure of problems? Can interventions and counterfactuals provide greater robot intelligence? And, ultimately, what next-generational capabilities can be unlocked through robots that can answer the question of “Why”?


8:50 - 9:00: Introduction

9:00 - 10:00: Session 1: Causal Learning and Representations

9:00 - 9:30: Peter Spirtes (CMU). Title: "Causal Learning Research: Achievements and Limitations"

9:30 - 10:00: Sara Magliacane (University of Amsterdam). Title: "Causal vs Causality-Inspired Representation Learning"

10:00 - 10:30: Break

10:30 - 11:30: Session 2: Foundations of Causality: Reasoning, Representations, Applications

10:30 - 11:00: Elias Bareinboim (Columbia). Title: "On the Causal Foundations of Artificial Intelligence (Explainability & Decision-Making)"

11:00 - 11:30: Francesco Locatello (ISTA). Title: "Foundations of Causal Representation Learning from Multiple Tasks and Sensors"

11:30 - 12:00: Rosemary Ke (DeepMind / Mila). Title: "Learning to Learn Causal Structure in Reinforcement Learning"

12:00 - 1:30: Lunch

1:30 - 2:30: Session 3: Reinforcement Learning and Representations

1:30 - 2:00: Biwei Huang (UCSD). Title: "Enhanced Reinforcement Learning through Causal Representations and Graph Structures"

2:00 - 2:30: Amy Zhang (UT Austin). Title: "Learning Invariant Representations for Out-of-Domain Generalization"

2:30 - 3:30: Poster Session

2:30 - 2:40: Lightning Talk (1 min per paper)

2:40 - 3:30: Poster Session

3:00 - 3:30: Break

3:30 - 4:30: Session 4: Causality and Robotics

3:30 - 4:00: Yunzhu Li (UIUC). Title: "Structural Causal Model Learning From and For Physical Interactions"

4:00 - 4:15: Ricardo Cannizzaro (Oxford). Title: "Causally-Informed Robot Planning in Partially-Observable and Confounded Environments"

4:15 - 4:30: Gokul Swamy (CMU). Title: "Causal Confounds in Imitation Learning"

4:30 - 5:00: Panel discussion: A "Fireside Chat" on Causality and Robotics with Biwei Huang and Yunzhu Li

5:00 - 5:05: Closing remarks

Accepted Papers

Topics of Interest

Broadly defined list of topics include, but are not limited to, the following

Call for Abstracts

Important Dates: 

Submission Info

We solicit 2 + N page extended abstracts conforming to the official IEEE Conference guidelines. A paper template is available in LaTeX and Word. The accepted papers can be extended to 3 + N for the camera-ready versions.

Submissions can include late-breaking results, materials under review, and archived or previously accepted work (please make a note of this in the submission).

Please note the accepted contributions will be presented in an interactive poster format (non-archival). A small set of these may be featured as spotlight or lightning talks. The accepted contributions and posters will be posted on the workshop website upon author approval.

Paper submissions should be made through OpenReview (submission link). The review process will be double-blind.



We are grateful to be supported by the IEEE RAS Robot Learning Technical Committee.