4 papers
CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functions
Lizhi Yang, Blake Werner, Massimiliano de Sa +1
Reinforcement learning (RL), while powerful and expressive, can often prioritize performance at the expense of safety. Yet safety violations can lead to catastrophic outcomes in re…
HALO: Hybrid Auto-encoded Locomotion with Learned Latent Dynamics, Poincaré Maps, and Regions of Attraction
Blake Werner, Sergio A. Esteban, Massimiliano De Sa +2
Reduced-order models are powerful for analyzing and controlling high-dimensional dynamical systems. Yet constructing these models for complex hybrid systems such as legged robots r…
SafeSpace: Aggregating Safe Sets from Backup Control Barrier Functions under Input Constraints
Pio Ong, David E. J. van Wijk, Massimiliano de Sa +2
Control barrier functions (CBFs) provide a principled framework for enforcing safety in control systems -- yet the certified safe operating region in practice is often conservative…
From Bundles to Backstepping: Geometric Control Barrier Functions for Safety-Critical Control on Manifolds
Massimiliano de Sa, Pio Ong, Aaron D. Ames
Control barrier functions (CBFs) have a well-established theory in Euclidean spaces, yet still lack general formulations and constructive synthesis tools for systems evolving on ma…