6 papers · 1 filter
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…
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…
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…