Large language models (LLMs) exhibit a wide range of promising capabilities--from step-by-
step planning to commonsense reasoning--that may provide utility for robots, but remain …

Black-box machine learning models are now routinely used in high-risk settings, like
medical diagnostics, which demand uncertainty quantification to avoid consequential model …

In recent years, formal methods have been extensively used in the design of autonomous
systems. By employing mathematically rigorous techniques, formal methods can provide …

Robotic applications often involve working in environments that are uncertain, dynamic, and
partially observable. Recently, diffusion models have been proposed for learning trajectory …

This paper proposes an algorithm for motion planning among dynamic agents using
adaptive conformal prediction. We consider a deterministic control system and use trajectory …

We introduce Conformal Decision Theory, a framework for producing safe autonomous
decisions despite imperfect machine learning predictions. Examples of such decisions are …

Black-box machine learning models are now routinely used in high-risk settings, like
medical diagnostics, which demand uncertainty quantification to avoid consequential model …

We are interested in predicting failures of cyber-physical systems during their operation.
Particularly, we consider stochastic systems and signal temporal logic specifications, and we …

Conformal prediction is a statistical tool for producing prediction regions of machine learning
models that are valid with high probability. However, applying conformal prediction to time …

Abstract Inductive Conformal Prediction (ICP) provides a practical and effective approach for
equipping deep learning models with uncertainty estimates in the form of set-valued …