3 citations · 6 across the 15 of their papers we have counts for
7 papers · 1 filter
Timescale Separation Through the Lens of Operator Theory
Guido Carnevale, Nicola Bastianello, Luca Schenato +2
Timescale separation is a powerful tool for analyzing interconnected dynamical systems. Meanwhile, operator theory provides a general framework for studying the convergence of iter…
Safe Learning Predictive Control for Ego-World Robotic Systems
Davide Valenti, Giuseppe Notarstefano
Safe autonomous navigation in shared environments requires the ability to anticipate and react to the latent behaviors of surrounding robots. In this paper, we propose SOWL-MPC, a…
Suboptimal and Reduced-Order MPC via Timescale Separation
Stefano Di Gregorio, Guido Carnevale, Giuseppe Notarstefano
In this paper, we propose a generalized framework for the design and analysis of suboptimal and reduced-order nonlinear Model Predictive Control (MPC) architectures. The proposed f…
On Reward-Balancing Methods for Reinforcement Learning
Simone Baroncini, Bahman Gharesifard, Giuseppe Notarstefano
This paper investigates the so-called reward-balancing methods, a novel class of algorithms for solving discounted-return reinforcement learning (RL) problems. These methods consis…
A Distributed Bilevel Framework for the Macroscopic Optimization of Multi-Agent Systems
Riccardo Brumali, Guido Carnevale, Sonia Martínez +1
In this paper, we propose a novel distributed algorithm to optimize the emergent macroscopic behavior of large-scale multi-agent systems via microscopic actions. We cast this task…
Model-Free Aggregative Cooperative Optimization via Randomized Gradient-Free Minimization and Exploration Momentum
Amir Mehrnoosh, Giuseppe Speciale, Riccardo Brumali +2
Aggregative cooperative optimization problems arise in distributed decision-making settings where each agent's objective depends on its own decision as well as on an aggregate vari…
Safe Control of Feedback-Interconnected Systems via Singular Perturbations
Stefano Di Gregorio, Guido Carnevale, Giuseppe Notarstefano
Control Barrier Functions (CBFs) have emerged as a powerful tool in the design of safety-critical controllers for nonlinear systems. In modern applications, complex systems often i…