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 …
M Ganai, S Gao, S Herbert - IEEE Open Journal of Control …, 2024 - ieeexplore.ieee.org
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 …