Rethinking Uncertainty for
Modern Robotics Paradigms

About the Workshop

Uncertainty quantification has long been a core aspect of robotics. It has improved the reliability of all components of the robotics pipeline, from perception, to state estimation, control, and planning. However, in the past five years, all these components have started to rely on–or be replaced with–extremely large pre-trained AI models, raising the question: what does uncertainty quantification mean in this new era? For instance, foundation models (e.g., LLMs, VLMs, VLAs, and world models) are trained on internet-scale datasets, typically lack explicit probabilistic representations, and their enormous parameter sizes make established uncertainty quantification techniques (e.g., ensembling) computationally impractical. Furthermore, even when these models are deployed as components within a modular autonomy pipeline, it remains difficult to understand how uncertainty at the component level influences overall system-level performance.

This workshop revisits the foundational problem of uncertainty quantification in light of these “modern robotics paradigms.” We aim to bring together a diverse group of academic, industrial, and government researchers focusing on uncertainty across all components of robot autonomy. Our goal is to chart a comprehensive roadmap for the next generation of uncertainty-aware robotics: (1) establishing a rigorous understanding of the new sources of uncertainty that can influence next-generation robotic systems, (2) exploring methods to quantify and mitigate uncertainty in the era of robotics foundation models, and (3) examining key challenges and future directions for building reliable, uncertainty-aware robotic systems.

Discussion Themes

• How should we quantify, calibrate, and mitigate uncertainty in robotic systems built on large pretrained models, including VLAs, VLMs, and world models?
• Are traditional uncertainty frameworks, such as Bayesian methods, still scalable and meaningful for modern robotics foundation models?
• What forms of verification, statistical guarantees, and reliability assessment are practical for learning-based robotic systems deployed in open-world settings?
• How should we reason about component-level versus system-level uncertainty, and which uncertainties actually matter for safe and reliable robot operation?

Invited Speakers

Max Simchowitz

Carnegie Mellon University

Dinesh Jayaraman

University of Pennsylvania

Zsolt Kira

Georgia Institute of Technology

Bernadette Bucher

University of Michigan

Haruki Nishimura

Toyota Research Institute

Susmit Jha

DARPA

Call for Papers

We invite short papers (4+n pages) submissions to share their findings with the community and engage in discussions on the past, present, and future directions of uncertainty-aware methods in robotics. We aim to bring together a diverse group of researchers—spanning machine learning theory, statistics, computer vision, mapping, decision-making, reinforcement learning, control theory, and robot learning—across all levels of embodied AI systems, with the shared goal of understanding and mitigating uncertainty for safe, trustworthy, and reliable robot operation. Topics of interest include, but are not limited to:

• Uncertainty Quantification for Robotic Foundation Models (VLAs, VAMs, World Models, etc.)
• Out-of-Distribution Detection
• Active and Continual Learning for Large Robotic Foundation Models
• Failure Detection and Recovery
• Verification, Calibration, and Reliability Assessment of Robotic Systems
• Statistical Guarantees of Robotic Systems
• Safety and Robustness of Learning-based Robotic Systems
• Evaluation for Robotic Foundation Models
• Planning and Control under Partial Observability

Non-archival: Sharing papers in this way does not constitute formal proceedings, i.e., this workshop is a non-archival venue that will not restrict later renditions of the work from being published in archival conferences or journals.

Important Dates

All deadlines are 23:59 AoE (Anywhere on Earth).

Schedule

Full-day program · September 27, 2026. All times are tentative.

Organizers

Brought to you by researchers across academia and industry.

Prasanna Sriganesh

Carnegie Mellon University

Junwon Seo

Carnegie Mellon University

Juyeop Han

Massachusetts Institute of Technology

Xiaoyi (Jeremy) Cai

Waymo

Zhiting (May) Mei

Princeton University

Rosario Scalise

University of Washington

Sertac Karaman

Massachusetts Institute of Technology

Andrea Bajcsy

Carnegie Mellon University

Matthew Travers

Carnegie Mellon University