collaborators

11 papers

cs.RO2026

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…

eess.SY2026

Safety Filtering with an Infinite Number of Constraints

Max H. Cohen, Pio Ong, Pol Mestres +1

Control barrier functions (CBFs) provide a rigorous framework for designing controllers enforcing safety constraints. While CBF theory is well-developed for a finite number of safe…

eess.SY2026

High Order Tuners for Adaptive Safety of Robotic Systems

Mohammad Mirtaba, Max H. Cohen

The combination of control barrier functions (CBFs) and adaptive control -- a framework referred to as adaptive safety -- has proven to be a powerful paradigm for safety-critical c…

eess.SY2026

Structure, Feasibility, and Explicit Safety Filters for Linear Systems

Shima Sadat Mousavi, Max H. Cohen, Pol Mestres +1

Safety filters based on control barrier functions (CBFs) and high-order control barrier functions (HOCBFs) are often implemented through quadratic programs (QPs). In general, espec…

cs.RO2026

Safety Guardrails in the Sky: Realizing Control Barrier Functions on the VISTA F-16 Jet

Andrew W. Singletary, Max H. Cohen, Tamas G. Molnar +1

The advancement of autonomous systems -- from legged robots to self-driving vehicles and aircraft -- necessitates executing increasingly high-performance and dynamic motions withou…

eess.SY2026

Input-to-State Safe Backstepping: Robust Safety-Critical Control with Unmatched Uncertainties

Max H. Cohen, Pio Ong, Aaron D. Ames

Guaranteeing safety in the presence of unmatched disturbances -- uncertainties that cannot be directly canceled by the control input -- remains a key challenge in nonlinear control…