323 citations · 407 across the 32 of their papers we have counts for
11 papers · 1 filter
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…