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

Quantifying a model's predictive uncertainty is essential for safety-critical applications such
as autonomous driving. We consider quantifying such uncertainty for multi-object detection …

One of the outstanding challenges for the widespread deployment of robotic systems like
autonomous vehicles is ensuring safe interaction with humans without sacrificing efficiency …

In this paper, we focus on the problem of shrinking-horizon Model Predictive Control (MPC)
in uncertain dynamic environments. We consider controlling a deterministic autonomous …

A particularly challenging problem in AI safety is providing guarantees on the behavior of
high-dimensional autonomous systems. Verification approaches centered around …

End-to-end learning has emerged as a major paradigm for developing autonomous
controllers. Unfortunately, with its performance and convenience comes an even greater …

An outstanding challenge for the widespread deployment of robotic systems like
autonomous vehicles is ensuring safe interaction with humans without sacrificing …

The control of dynamical systems under temporal logic specifications among uncontrollable
dynamic agents is challenging due to the agents'a-priori unknown behavior. Existing works …

The interest in using reinforcement learning (RL) controllers in safety-critical applications
such as robot navigation around pedestrians motivates the development of additional safety …

Safe and effective planning in cluttered and diverse scenes that include the presence of
other agents, requires a robot that is (1) equipped with a state-of-the-art autonomy stack and …