323 citations · 407 across the 32 of their papers we have counts for
45 papers
Failure-Aware Iterative Learning of State-Control Invariant Sets
Ahmad Amine, Nick-Marios T. Kokolakis, Ugo Rosolia +2
In this paper, we address the problem of computing maximal state-control invariant sets for deterministic linear systems using failing trajectories. We introduce the concept of sta…
SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks
Zirui Zang, Ahmad Amine, Nick-Marios T. Kokolakis +3
Robots executing iterative tasks in complex, uncertain environments require control strategies that balance robustness, safety, and high performance. This paper introduces a safe i…
Speed-Aware Network Design: A Parametric Optimization Approach
Ugo Rosolia, Marc Bataillou Almagro, George Iosifidis +2
Network design problems have been studied from the 1950s, as they can be used in a wide range of real-world applications, e.g., design of communication and transportation networks.…
A Constructive Method for Designing Safe Multirate Controllers for Differentially-Flat Systems
Devansh R. Agrawal, Hardik Parwana, Ryan K. Cosner +3
We present a multi-rate control architecture that leverages fundamental properties of differential flatness to synthesize controllers for safety-critical nonlinear dynamical system…
Ensemble Gaussian Processes for Adaptive Autonomous Driving on Multi-friction Surfaces
Tomáš Nagy, Ahmad Amine, Truong X. Nghiem +3
Driving under varying road conditions is challenging, especially for autonomous vehicles that must adapt in real-time to changes in the environment, e.g., rain, snow, etc. It is di…
Solving Recurrent MIPs with Semi-supervised Graph Neural Networks
Konstantinos Benidis, Ugo Rosolia, Syama Rangapuram +2
We propose an ML-based model that automates and expedites the solution of MIPs by predicting the values of variables. Our approach is motivated by the observation that many problem…
Multi-Rate Planning and Control of Uncertain Nonlinear Systems: Model Predictive Control and Control Lyapunov Functions
Noel Csomay-Shanklin, Andrew J. Taylor, Ugo Rosolia +1
Modern control systems must operate in increasingly complex environments subject to safety constraints and input limits, and are often implemented in a hierarchical fashion with di…
MLNav: Learning to Safely Navigate on Martian Terrains
Shreyansh Daftry, Neil Abcouwer, Tyler Del Sesto +7
We present MLNav, a learning-enhanced path planning framework for safety-critical and resource-limited systems operating in complex environments, such as rovers navigating on Mars.…
Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing
Johannes Betz, Hongrui Zheng, Alexander Liniger +5
The rising popularity of self-driving cars has led to the emergence of a new research field in the recent years: Autonomous racing. Researchers are developing software and hardware…
CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning
Kevin Huang, Sahin Lale, Ugo Rosolia +2
Current state-of-the-art model-based reinforcement learning algorithms use trajectory sampling methods, such as the Cross-Entropy Method (CEM), for planning in continuous control s…
Mixed Observable RRT: Multi-Agent Mission-Planning in Partially Observable Environments
Kasper Johansson, Ugo Rosolia, Wyatt Ubellacker +2
This paper considers centralized mission-planning for a heterogeneous multi-agent system with the aim of locating a hidden target. We propose a mixed observable setting, consisting…
Interactive multi-modal motion planning with Branch Model Predictive Control
Yuxiao Chen, Ugo Rosolia, Wyatt Ubellacker +2
Motion planning for autonomous robots and vehicles in presence of uncontrolled agents remains a challenging problem as the reactive behaviors of the uncontrolled agents must be con…
Risk-Averse Decision Making Under Uncertainty
Mohamadreza Ahmadi, Ugo Rosolia, Michel D. Ingham +2
A large class of decision making under uncertainty problems can be described via Markov decision processes (MDPs) or partially observable MDPs (POMDPs), with application to artific…
Learning Performance Bounds for Safety-Critical Systems
Prithvi Akella, Ugo Rosolia, Aaron D. Ames
As the complexity of control systems increases, the need for systematic methods to guarantee their efficacy grows as well. However, direct testing of these systems is oftentimes co…
The Mixed-Observable Constrained Linear Quadratic Regulator Problem: the Exact Solution and Practical Algorithms
Ugo Rosolia, Yuxiao Chen, Shreyansh Daftry +3
This paper studies the problem of steering a linear time-invariant system subject to state and input constraints towards a goal location that may be inferred only through partial o…
Iterative Model Predictive Control for Piecewise Systems
Ugo Rosolia, Aaron D. Ames
In this paper, we present an iterative Model Predictive Control (MPC) design for piecewise nonlinear systems. We consider finite time control tasks where the goal of the controller…
A Simple Robust MPC for Linear Systems with Parametric and Additive Uncertainty
Monimoy Bujarbaruah, Ugo Rosolia, Yvonne R. Stürz +1
We propose a simple and computationally efficient approach for designing a robust Model Predictive Controller (MPC) for constrained uncertain linear systems. The uncertainty is mod…
Multi-rate Control Design under Input Constraints via Fixed-Time Barrier Functions
Kunal Garg, Ryan K. Cosner, Ugo Rosolia +2
In this paper, we introduce the notion of periodic safety, which requires that the system trajectories periodically visit a subset of a forward-invariant safe set, and utilize it i…
Time-Optimal Navigation in Uncertain Environments with High-Level Specifications
Ugo Rosolia, Mohamadreza Ahmadi, Richard M. Murray +1
Mixed observable Markov decision processes (MOMDPs) are a modeling framework for autonomous systems described by both fully and partially observable states. In this work, we study…
Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive Control
Ivan D. Jimenez Rodriguez, Ugo Rosolia, Aaron D. Ames +1
We present a straightforward and efficient way to control unstable robotic systems using an estimated dynamics model. Specifically, we show how to exploit the differentiability of…
Unified Multi-Rate Control: from Low Level Actuation to High Level Planning
Ugo Rosolia, Andrew Singletary, Aaron D. Ames
In this paper we present a hierarchical multi-rate control architecture for nonlinear autonomous systems operating in partially observable environments. Control objectives are expr…
Constrained Risk-Averse Markov Decision Processes
Mohamadreza Ahmadi, Ugo Rosolia, Michel D. Ingham +2
We consider the problem of designing policies for Markov decision processes (MDPs) with dynamic coherent risk objectives and constraints. We begin by formulating the problem in a L…
Reactive motion planning with probabilistic safety guarantees
Yuxiao Chen, Ugo Rosolia, Chuchu Fan +2
Motion planning in environments with multiple agents is critical to many important autonomous applications such as autonomous vehicles and assistive robots. This paper considers th…
Decentralized Task and Path Planning for Multi-Robot Systems
Yuxiao Chen, Ugo Rosolia, Aaron D. Ames
We consider a multi-robot system with a team of collaborative robots and multiple tasks that emerges over time. We propose a fully decentralized task and path planning (DTPP) frame…