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

We propose a framework for planning in unknown dynamic environments with probabilistic
safety guarantees using conformal prediction. Particularly, we design a model predictive …

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

Trajectory prediction is a critical functionality of autonomous systems that share
environments with uncontrolled agents, one prominent example being self-driving vehicles …

Autonomous driving is considered one of the revolutionary technologies sha** humanity's
future mobility and quality of life. However, safety remains a critical hurdle in the way of …

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

Autonomous vehicle (AV) stacks are typically built in a modular fashion, with explicit
components performing detection, tracking, prediction, planning, control, etc. While …

Motion planning for autonomous robots and vehicles in presence of uncontrolled agents
remains a challenging problem as the reactive behaviors of the uncontrolled agents must be …

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

When deploying machine learning models in high-stakes robotics applications, the ability to
detect unsafe situations is crucial. Early warning systems can provide alerts when an unsafe …