2 citations · 3 across the 17 of their papers we have counts for
19 papers
A Unified Perspective on Conformal Prediction and Wasserstein Distributionally Robust Optimization for Uncertainty Quantification
Kehan Long, Yiqi Zhao, Pol Mestres +3
Uncertainty quantification from finite data is central to machine learning, optimization, and automation systems, where decisions must remain reliable under limited samples and tes…
Safe Feedback Optimization through Control Barrier Functions
Giannis Delimpaltadakis, Pol Mestres, Jorge Cortés +1
Feedback optimization refers to a class of methods that steer a control system to a steady state that solves an optimization problem. Despite tremendous progress on the topic, an i…
Off-Policy Reinforcement Learning with Anytime Safety Guarantees via Robust Safe Gradient Flow
Pol Mestres, Arnau Marzabal, Jorge Cortés
This paper considers the problem of solving constrained reinforcement learning (RL) problems with anytime guarantees, meaning that the algorithmic solution must yield a constraint-…
Universal Formulas for Safe Control and Their Neural Network Approximations
Pol Mestres, Jorge Cortés, Eduardo D. Sontag
We study the problem of designing a controller that satisfies an arbitrary number of affine inequalities at every point in the state space. This is motivated by the fact that a var…
Anytime Safe Reinforcement Learning
Pol Mestres, Arnau Marzabal, Jorge Cortés
This paper considers the problem of solving constrained reinforcement learning problems with anytime guarantees, meaning that the algorithmic solution returns a safe policy regardl…
Feedback Optimization with State Constraints through Control Barrier Functions
Giannis Delimpaltadakis, Pol Mestres, Jorge Cortés +1
Recently, there has been a surge of research on a class of methods called feedback optimization. These are methods to steer the state of a control system to an equilibrium that ari…
Control Barrier Function-Based Safety Filters: Characterization of Undesired Equilibria, Unbounded Trajectories, and Limit Cycles
Pol Mestres, Yiting Chen, Emiliano Dall'anese +1
This paper focuses on safety filters designed based on Control Barrier Functions (CBFs): these are modifications of a nominal stabilizing controller typically utilized in safety-cr…
Safe and Dynamically-Feasible Motion Planning using Control Lyapunov and Barrier Functions
Pol Mestres, Carlos Nieto-Granda, Jorge Cortés
This paper considers the problem of designing motion planning algorithms for control-affine systems that generate collision-free paths from an initial to a final destination and ca…
Independence of Closed-Loop Equilibria and Stability from the Choice of Control Barrier Function for a Given Safe Set
Yiting Chen, Pol Mestres, Jorge Cortes +1
Control barrier functions (CBFs) play a critical role in the design of safe optimization-based controllers for control-affine systems. Given a CBF associated with a given, predefin…
Characterization of the Dynamical Properties of Safety Filters for Linear Planar Systems
Yiting Chen, Pol Mestres, Emiliano Dall'Anese +1
This paper studies the dynamical properties of closed-loop systems obtained from control barrier function-based safety filters. We provide a sufficient and necessary condition for…
Stabilization of Nonlinear Systems through Control Barrier Functions
Pol Mestres, Kehan Long, Melvin Leok +2
This paper proposes a control design approach for stabilizing nonlinear control systems. Our key observation is that the set of points where the decrease condition of a control Lya…
Converse Theorems for Certificates of Safety and Stability
Pol Mestres, Jorge Cortés
Motivated by the key role of control barrier functions (CBFs) in assessing safety and enabling the synthesis of safe controllers in nonlinear control systems, this paper presents a…
Distributed Safe Navigation of Multi-Agent Systems using Control Barrier Function-Based Optimal Controllers
Pol Mestres, Carlos Nieto-Granda, Jorge Cortés
This paper proposes a distributed controller synthesis framework for safe navigation of multi-agent systems. We leverage control barrier functions to formulate collision avoidance…
Feasibility Analysis and Regularity Characterization of Distributionally Robust Safe Stabilizing Controllers
Pol Mestres, Kehan Long, Nikolay Atanasov +1
This paper studies the well-posedness and regularity of safe stabilizing optimization-based controllers for control-affine systems in the presence of model uncertainty. When the sy…
Regularity Properties of Optimization-Based Controllers
Pol Mestres, Ahmed Allibhoy, Jorge Cortés
This paper studies regularity properties of optimization-based controllers, which are obtained by solving optimization problems where the parameter is the system state and the opti…
Distributed and Anytime Algorithm for Network Optimization Problems with Separable Structure
Pol Mestres, Jorge Cortés
This paper considers the problem of designing a dynamical system to solve constrained optimization problems in a distributed way and in an anytime fashion (i.e., such that the feas…
Feasibility and Regularity Analysis of Safe Stabilizing Controllers under Uncertainty
Pol Mestres, Jorge Cortés
This paper studies the problem of safe stabilization of control-affine systems under uncertainty. Our starting point is the availability of worst-case or probabilistic error descri…
Optimization-Based Safe Stabilizing Feedback with Guaranteed Region of Attraction
Pol Mestres, Jorge Cortés
This paper proposes an optimization with penalty-based feedback design framework for safe stabilization of control affine systems. Our starting point is the availability of a contr…
Resource-Aware Discretization of Accelerated Optimization Flows
Miguel Vaquero, Pol Mestres, Jorge Cortés
This paper tackles the problem of discretizing accelerated optimization flows while retaining their convergence properties. Inspired by the success of resource-aware control in dev…