no code implementations • 19 May 2024 • Siddharth H. Nair, Charlott Vallon, Francesco Borrelli
Multi-Objective Learning Model Predictive Control is a novel data-driven control scheme which improves a system's closed-loop performance with respect to several control objectives over iterations of a repeated task.
no code implementations • 21 Mar 2024 • Charlott Vallon, Mark Pustilnik, Alessandro Pinto, Francesco Borrelli
This paper focuses on the design of hierarchical control architectures for autonomous systems with energy constraints.
no code implementations • 20 Mar 2024 • Mark Pustilnik, Francesco Borrelli
The paper presents an approach to solving the Robust Energy Capacitated Vehicle Routing Problem (RECVRP), focusing on electric vehicles and their limited battery capacity.
no code implementations • 22 Feb 2024 • Xu Shen, Yongkeun Choi, Alex Wong, Francesco Borrelli, Scott Moura, Soomin Woo
This paper introduces a novel approach to optimize the parking efficiency for fleets of Connected and Automated Vehicles (CAVs).
1 code implementation • 2 Feb 2024 • Hansung Kim, Siddharth H. Nair, Francesco Borrelli
We propose a hierarchical architecture designed for scalable real-time Model Predictive Control (MPC) in complex, multi-modal traffic scenarios.
no code implementations • 1 Feb 2024 • Eunhyek Joa, Eric Yongkeun Choi, Francesco Borrelli
Our method demonstrates $12\%$ improvement in energy efficiency compared to the traditional approaches, which plan longitudinal speed by solving a long-horizon optimal control problem and track the planned speed using another controller, as evidenced by vehicle experiments.
no code implementations • 24 May 2023 • Jacopo Guanetti, Yeojun Kim, Xu Shen, Joel Donham, Santosh Alexander, Bruce Wootton, Francesco Borrelli
These predictions are then used by a Predictive Block Assignment module to maximize the BEB fleet utilization.
1 code implementation • 21 Mar 2023 • Siddharth H. Nair, Francesco Borrelli
We propose an iterative approach for designing Robust Learning Model Predictive Control (LMPC) policies for a class of nonlinear systems with additive, unmodelled dynamics.
1 code implementation • 21 Mar 2023 • Luigi Russo, Siddharth H. Nair, Luigi Glielmo, Francesco Borrelli
We propose a supervised learning framework for computing solutions of multi-parametric Mixed Integer Linear Programs (MILPs) that arise in Model Predictive Control.
no code implementations • 21 Feb 2023 • Eunhyek Joa, Monimoy Bujarbaruah, Francesco Borrelli
We present an output feedback stochastic model predictive controller (SMPC) for constrained linear time-invariant systems.
no code implementations • 29 Nov 2022 • Paula Chanfreut, José María Maestre, Eduardo F. Camacho, Francesco Borrelli
This paper presents a cloud-based learning model predictive controller that integrates three interacting components: a set of agents, which must learn to perform a finite set of tasks with the minimum possible local cost; a coordinator, which assigns the tasks to the agents; and the cloud, which stores data to facilitate the agents' learning.
no code implementations • 21 Sep 2022 • Hotae Lee, Monimoy Bujarbaruah, Francesco Borrelli
A sample-based strategy is used to compute sets of disturbance sequences necessary for robustifying the state chance constraints.
no code implementations • 6 Aug 2022 • Siddharth H. Nair, Vijay Govindarajan, Theresa Lin, Yan Wang, Eric H. Tseng, Francesco Borrelli
The proposed approach is demonstrated on a longitudinal control example, with uncertainties in predictions of the autonomous and surrounding vehicles.
1 code implementation • 22 Apr 2022 • Thomas Fork, H. Eric Tseng, Francesco Borrelli
We leverage game theory and a new vehicle modeling approach to compute overtaking maneuvers for racecars on a nonplanar surface.
1 code implementation • 20 Apr 2022 • Thomas Fork, H. Eric Tseng, Francesco Borrelli
We present a 10 DoF dynamic vehicle model for model-based control on nonplanar road surfaces.
1 code implementation • 17 Apr 2022 • Xu Shen, Matthew Lacayo, Nidhir Guggilla, Francesco Borrelli
The problem of multimodal intent and trajectory prediction for human-driven vehicles in parking lots is addressed in this paper.
1 code implementation • 30 Mar 2022 • Edward L. Zhu, Francesco Borrelli
Dynamic games can be an effective approach to modeling interactive behavior between multiple non-cooperative agents and they provide a theoretical framework for simultaneous prediction and control in such scenarios.
no code implementations • 20 Sep 2021 • Siddharth H. Nair, Vijay Govindarajan, Theresa Lin, Chris Meissen, H. Eric Tseng, Francesco Borrelli
The use of feedback policies for prediction is motivated by the need for reduced conservatism in handling multi-modal predictions of the surrounding vehicles, especially prevalent in traffic intersection scenarios.
no code implementations • 13 Sep 2021 • Eunhyek Joa, Yibo Sun, Francesco Borrelli
The result is a simple, real-time localization method using an image retrieval method whose performance is comparable to other monocular camera localization methods which use a map built with LiDARs.
no code implementations • 18 Aug 2021 • Yeojun Kim, Jacopo Guanetti, Francesco Borrelli
This paper studies the value of communicated motion predictions in the longitudinal control of connected automated vehicles (CAVs).
1 code implementation • NeurIPS 2021 • Jeffrey Ichnowski, Paras Jain, Bartolomeo Stellato, Goran Banjac, Michael Luo, Francesco Borrelli, Joseph E. Gonzalez, Ion Stoica, Ken Goldberg
First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapidly solved.
no code implementations • 13 May 2021 • Charlott Vallon, Francesco Borrelli
In addition to task-invariant system state and input constraints, a parameterized environment model generates task-specific state constraints, which are satisfied by the stored trajectories.
1 code implementation • 17 Apr 2021 • Thomas Fork, H. Eric Tseng, Francesco Borrelli
We present a simplified model of a vehicle driving on a nonplanar road.
1 code implementation • 23 Mar 2021 • Monimoy Bujarbaruah, Ugo Rosolia, Yvonne R. Stürz, Francesco Borrelli
We propose a simple and computationally efficient approach for designing a robust Model Predictive Controller (MPC) for constrained uncertain linear systems.
no code implementations • 20 Nov 2020 • Hotae Lee, Monimoy Bujarbaruah, Francesco Borrelli
"Bubble Ball" is a game built on a 2D physics engine, where a finite set of objects can modify the motion of a bubble-like ball.
1 code implementation • 19 Jul 2020 • Monimoy Bujarbaruah, Tony Zheng, Akhil Shetty, Martin Sehr, Francesco Borrelli
In this paper, we present a learning model based control strategy for the cup-and-ball game, where a Universal Robots UR5e manipulator arm learns to catch a ball in one of the cups on a Kendama.
2 code implementations • 2 Jul 2020 • Monimoy Bujarbaruah, Ugo Rosolia, Yvonne R Stürz, Xiaojing Zhang, Francesco Borrelli
We propose a novel approach to design a robust Model Predictive Controller (MPC) for constrained uncertain linear systems.
1 code implementation • 9 Jun 2020 • Monimoy Bujarbaruah, Charlott Vallon, Francesco Borrelli
We propose a control design method for linear time-invariant systems that iteratively learns to satisfy unknown polyhedral state constraints.
no code implementations • 21 Apr 2020 • Xu Shen, Ivo Batkovic, Vijay Govindarajan, Paolo Falcone, Trevor Darrell, Francesco Borrelli
We investigate the problem of predicting driver behavior in parking lots, an environment which is less structured than typical road networks and features complex, interactive maneuvers in a compact space.
1 code implementation • 2 Apr 2020 • Edward L. Zhu, Yvonne R. Stürz, Ugo Rosolia, Francesco Borrelli
We present a decentralized minimum-time trajectory optimization scheme based on learning model predictive control for multi-agent systems with nonlinear decoupled dynamics and coupled state constraints.
1 code implementation • 22 Nov 2019 • Monimoy Bujarbaruah, Akhil Shetty, Kameshwar Poolla, Francesco Borrelli
We propose an approach to design a Model Predictive Controller (MPC) for constrained Linear Time Invariant systems performing an iterative task.
no code implementations • 21 Nov 2019 • Ugo Rosolia, Xiaojing Zhang, Francesco Borrelli
At each iteration of the control task the closed-loop state, input and cost are stored and used in the controller design.
no code implementations • 30 Sep 2019 • Monimoy Bujarbaruah, Xiaojing Zhang, Marko Tanaskovic, Francesco Borrelli
We consider a linear system, in presence of bounded time varying additive uncertainty.
1 code implementation • 19 Jun 2019 • Xiaojing Zhang, Monimoy Bujarbaruah, Francesco Borrelli
In contrast to most existing approaches, we not only learn the control policy, but also a "certificate policy", that allows us to estimate the sub-optimality of the learned control policy online, during execution-time.
no code implementations • 31 May 2019 • Brijen Thananjeyan, Ashwin Balakrishna, Ugo Rosolia, Felix Li, Rowan Mcallister, Joseph E. Gonzalez, Sergey Levine, Francesco Borrelli, Ken Goldberg
Reinforcement learning (RL) for robotics is challenging due to the difficulty in hand-engineering a dense cost function, which can lead to unintended behavior, and dynamical uncertainty, which makes exploration and constraint satisfaction challenging.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 16 Mar 2019 • Charlott Vallon, Francesco Borrelli
A task decomposition method for iterative learning model predictive control is presented.
no code implementations • 3 Mar 2017 • Y ang Zheng, Shengbo Eben Li, Keqiang Li, Francesco Borrelli
This paper presents a distributed model predictive control (DMPC) algorithm for heterogeneous vehicle platoons with unidirectional topologies and a p r i o r i unknown desired set point.
no code implementations • 23 Feb 2017 • Ugo Rosolia, Francesco Borrelli
The control scheme is reference-free and is able to improve its performance by learning from previous iterations.
no code implementations • 20 Oct 2016 • Ugo Rosolia, Ashwin Carvalho, Francesco Borrelli
A novel learning Model Predictive Control technique is applied to the autonomous racing problem.
no code implementations • 6 Sep 2016 • Ugo Rosolia, Francesco Borrelli
The controller is reference-free and is able to improve its performance by learning from previous iterations.