5 papers · 1 filter
Refining Almost-Safe Value Functions on the Fly
Sander Tonkens, Sosuke Kojima, Chenhao Liu +2
Control Barrier Functions (CBFs) are a powerful tool for ensuring robotic safety, but designing or learning valid CBFs for complex systems is a significant challenge. While Hamilto…
MADR: MPC-guided Adversarial DeepReach
Ryan Teoh, Sander Tonkens, William Sharpless +4
Hamilton-Jacobi (HJ) Reachability offers a framework for generating safe value functions and policies in the face of adversarial disturbance, but is limited by the curse of dimensi…
From Space to Time: Enabling Adaptive Safety with Learned Value Functions via Disturbance Recasting
Sander Tonkens, Nikhil Uday Shinde, Azra Begzadić +3
The widespread deployment of autonomous systems in safety-critical environments such as urban air mobility hinges on ensuring reliable, performant, and safe operation under varying…
Reachability Barrier Networks: Learning Hamilton-Jacobi Solutions for Smooth and Flexible Control Barrier Functions
Matthew Kim, William Sharpless, Hyun Joe Jeong +3
Recent developments in autonomous driving and robotics underscore the necessity of safety-critical controllers. Control barrier functions (CBFs) are a popular method for appending…
Patching Approximately Safe Value Functions Leveraging Local Hamilton-Jacobi Reachability Analysis
Sander Tonkens, Alex Toofanian, Zhizhen Qin +2
Safe value functions, such as control barrier functions, characterize a safe set and synthesize a safety filter, overriding unsafe actions, for a dynamic system. While function app…