1st Workshop on Data Meets Physics:
Combining Data-Driven and Physics-Based Methods in Robotics

Machine learning is reshaping robotics, enabling new capabilities in modeling, decision-making, perception and control. However, purely data-driven methods often struggle to deliver generalizable performance in safety-critical applications, where limited training data is available. To address this, recent research has been exploring ways to embed physics-based and classical engineering principles into learning architectures, enhancing interpretability, generalization and sample efficiency. This has led to physics-driven learning paradigms that merge the strengths of data-driven and model-based approaches.

The workshop Data Meets Physics at IROS 2026 will bring together researchers from robotics, machine learning, and control to examine the opportunities and challenges of combining physics-based and data-driven methods. Discussions will focus on embedding domain knowledge into learning frameworks, moving beyond black-box models toward hybrid approaches that couple data with physical insight. Through invited talks, a round table, and a poster session, the workshop will foster knowledge exchange among professors, early-career researchers, and practitioners, encouraging collaboration and innovation in this rapidly-evolving field.

The workshop will involve five invited talks from leading professors, early-career researchers, and industry practitioners. A poster spotlight will give authors of contributed papers a 2-minute pitch, leading to a poster session.

The workshop will feature a round-table panel on Physics-Aware Learning: from Small Neural Networks to World Foundation Models. The workshop will be held in-person in Pittsburgh, USA. Additionally, we welcome the audience to join virtually via Zoom.

This workshop aims to explore the synergies between data-driven and physics-based approaches in robotics. We invite submissions related to the following topics:

• Beyond black-box learning: How can we fuse physics-based knowledge with data-driven learning to enhance accuracy and generalization?
• Physics-informed, physics-encoded, physics-guided machine learning and neural operators: How can domain knowledge be injected into training loss functions, model architectures, constraints, or synthetic data generation pipelines?
• Physics-grounded foundation models for embodied intelligence: Can large-scale models be constrained or guided by physics to ensure safety, trustworthiness, and transferability to embodied systems?
• From correlation to causation: How can physics-driven machine learning approaches discover causal structures and enhance interpretability in decision-making?
• Applications and challenges across robotics domains: From modeling and state estimation to planning, control, and decision-making.

Papers must be prepared according to the IROS'26 format, and can have 2-8 pages. We also encourage submitting new ideas, even if not fully developed yet.

Papers will be evaluated for quality, relevance to the workshop theme, clarity, and whether claims are well-supported by theory or experiments.

All accepted contributions will be presented as posters during our poster session. Accepted papers will be posted on the workshop website, and will not be part of the IROS conference proceedings.

A Best Paper Award will be given to the best contributed paper, selected by the program committee based on its quality and significance in the context of the workshop topics. The awardee will receive a certificate in recognition of their contribution.