We survey applications of pretrained foundation models in robotics. Traditional deep
learning models in robotics are trained on small datasets tailored for specific tasks, which …

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

Building general-purpose robots that can operate seamlessly, in any environment, with any
object, and utilizing various skills to complete diverse tasks has been a long-standing goal in …

The ability to accurately predict others' behavior is central to the safety and efficiency of
robotic systems in interactive settings, such as human–robot interaction and multi-robot …

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

Ensuring robot safety in complex environments is a difficult task due to actuation limits, such
as torque bounds. This paper presents a safety-critical control framework that leverages …

Recent literature has proposed approaches that learn control policies with high performance
while maintaining safety guarantees. Synthesizing Hamilton-Jacobi (HJ) reachable sets has …

Robot learning methods have recently made great strides, but generalization and
robustness challenges still hinder their widespread deployment. Failing to detect and …

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

Legged locomotion holds the premise of universal mobility, a critical capability for many real-
world robotic applications. Both model-based and learning-based approaches have …