9 papers
Safe-SAGE: Social-Semantic Adaptive Guidance for Safe Engagement through Laplace-Modulated Poisson Safety Functions
Lizhi Yang, Ryan M. Bena, Meg Wilkinson +4
Traditional safety-critical control methods, such as control barrier functions, suffer from semantic blindness, exhibiting the same behavior around obstacles regardless of contextu…
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…
MARCH: Model-Assisted Reinforcement Learning for the Perceptive Control of Humanoids over Sparse Footholds
Codrin Crismariu, Ryan K. Cosner
Perceptive bipedal locomotion over sparse terrain remains a difficult challenge: model-based methods are precise but brittle to uncertainty, while model-free methods are robust but…
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…
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…
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…