124 citations · 200 across the 25 of their papers we have counts for
6 papers · 1 filter
Risk-Aware Safety Filters with Poisson Safety Functions and Laplace Guidance Fields
Gilbert Bahati, Ryan M. Bena, Meg Wilkinson +3
Robotic systems navigating in real-world settings require a semantic understanding of their environment to properly determine safe actions. This work aims to develop the mathematic…
Probabilistic Control Barrier Functions: Safety in Probability for Discrete-Time Stochastic Systems
Pol Mestres, Blake Werner, Ryan K. Cosner +1
Control systems operating in the real world face countless sources of unpredictable uncertainties. These random disturbances can render deterministic guarantees inapplicable and ca…
Geometry-Aware Predictive Safety Filters on Humanoids: From Poisson Safety Functions to CBF Constrained MPC
Ryan M. Bena, Gilbert Bahati, Blake Werner +3
Autonomous navigation through unstructured and dynamically-changing environments is a complex task that continues to present many challenges for modern roboticists. In particular,…
Learning Safe Control via On-the-Fly Bandit Exploration
Alexandre Capone, Ryan Cosner, Aaaron Ames +1
Control tasks with safety requirements under high levels of model uncertainty are increasingly common. Machine learning techniques are frequently used to address such tasks, typica…
SHIELD: Safety on Humanoids via CBFs In Expectation on Learned Dynamics
Lizhi Yang, Blake Werner, Ryan K. Cosner +3
Robot learning has produced remarkably effective ``black-box'' controllers for complex tasks such as dynamic locomotion on humanoids. Yet ensuring dynamic safety, i.e., constraint…
Control Barrier Function Synthesis for Nonlinear Systems with Dual Relative Degree
Gilbert Bahati, Ryan K. Cosner, Max H. Cohen +2
Control barrier functions (CBFs) are a powerful tool for synthesizing safe control actions; however, constructing CBFs remains difficult for general nonlinear systems. In this work…