Author name cluster

Francesco Borrelli

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8 papers

2 author rows

IROS Conference 2025 Conference Paper

A Simple Approach to Constraint-Aware Imitation Learning with Application to Autonomous Racing

Shengfan Cao
Eunhyek Joa
Francesco Borrelli

Guaranteeing constraint satisfaction is challenging in imitation learning (IL), particularly in tasks that require operating near a system’s handling limits. Traditional IL methods, such as Behavior Cloning (BC), often struggle to enforce constraints, leading to suboptimal performance in high-precision tasks. In this paper, we present a simple approach to incorporating safety into the IL objective. Through simulations, we empirically validate our approach on an autonomous racing task with both full-state and image feedback, demonstrating improved constraint satisfaction and greater consistency in task performance compared to BC.

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ICRA Conference 2024 Conference Paper

Learning Model Predictive Control with Error Dynamics Regression for Autonomous Racing

Haoru Xue
Edward L. Zhu
John M. Dolan
Francesco Borrelli

This work presents a novel Learning Model Predictive Control (LMPC) strategy for autonomous racing at the handling limit that can iteratively explore and learn unknown dynamics in high-speed operational domains. We start from existing LMPC formulations and modify the system dynamics learning method. In particular, our approach uses a nominal, global, nonlinear, physics-based model with a local, linear, data-driven learning of the error dynamics. We conducted experiments in simulation and on 1/10th scale hardware, and deployed the proposed LMPC on a full-scale autonomous race car used in the Indy Autonomous Challenge (IAC) with closed loop experiments at the Putnam Park Road Course in Indiana, USA. The results show that the proposed control policy exhibits improved robustness to parameter tuning and data scarcity. Incremental and safety-aware exploration toward the limit of handling and iterative learning of the vehicle dynamics in high-speed domains is observed both in simulations and experiments.

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IROS Conference 2023 Conference Paper

A Gaussian Process Model for Opponent Prediction in Autonomous Racing

Edward L. Zhu
Finn Lukas Busch
Jake Johnson
Francesco Borrelli

In head-to-head racing, performing tightly con-strained, but highly rewarding maneuvers, such as overtaking, require an accurate model of interactive behavior of the opposing target vehicle (TV). We propose to construct a prediction model given data of the TV from previous races. In particular, a one-step Gaussian process (GP) model is trained on closed-loop interaction data to learn the behavior of a TV driven by an unknown policy. Predictions of the nominal trajectory and associated uncertainty are rolled out via a sampling-based approach and are used in a model predictive control (MPC) policy for the ego vehicle in order to intelligently trade-off between safety and performance when racing against a TV. In a Monte Carlo study, we compare the GP-based predictor in closed-loop with the MPC policy against several predictors from literature and observe that the GP-based predictor achieves similar win rates while maintaining safety in up to 3x more races. Through experiments, we demonstrate the approach in real-time on a 1/10th scale racecar platform operating at speeds of around 2. 8 m/s, and show a significant level of improvement when using the GP-based predictor over a baseline MPC predictor. Videos of the experiments can be found at https://voutu.be/KMSs4ofDfIs.

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ICRA Conference 2023 Conference Paper

A Sequential Quadratic Programming Approach to the Solution of Open-Loop Generalized Nash Equilibria

Edward L. Zhu
Francesco Borrelli

In this work, we propose a numerical method for the solution of local generalized Nash equilibria (GNE) for the class of open-loop general-sum dynamic games for agents with nonlinear dynamics and constraints. In particular, we formulate a sequential quadratic programming (SQP) approach which requires only the solution of a single convex quadratic program at each iteration and is locally convergent. Central to the effectiveness of our approach is a non-monotonic line search method and a novel merit function for SQP step acceptance which helps to improve solver convergence beyond the local neighborhood of a GNE. We demonstrate the effectiveness of the algorithm in the context of car racing, where we see up to 32% improvement of success rate when comparing against a recent solution approach for dynamic games. We also make our code available at https://github.com/zhu-edward/DGSQP.

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NeurIPS Conference 2021 Conference Paper

Accelerating Quadratic Optimization with Reinforcement Learning

Jeffrey Ichnowski
Paras Jain
Bartolomeo Stellato
Goran Banjac
Michael Luo
Francesco Borrelli
Joseph E. Gonzalez
Ion Stoica

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. These methods face two persistent challenges: manual hyperparameter tuning and convergence time to high-accuracy solutions. To address these, we explore how Reinforcement Learning (RL) can learn a policy to tune parameters to accelerate convergence. In experiments with well-known QP benchmarks we find that our RL policy, RLQP, significantly outperforms state-of-the-art QP solvers by up to 3x. RLQP generalizes surprisingly well to previously unseen problems with varying dimension and structure from different applications, including the QPLIB, Netlib LP and Maros-M{\'e}sz{\'a}ros problems. Code, models, and videos are available at https: //berkeleyautomation. github. io/rlqp/.

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ICRA Conference 2021 Conference Paper

Collision Avoidance in Tightly-Constrained Environments without Coordination: a Hierarchical Control Approach

Xu Shen 0003
Edward L. Zhu
Yvonne R. Stürz
Francesco Borrelli

We present a hierarchical control approach for maneuvering an autonomous vehicle (AV) in tightly-constrained environments where other moving AVs and/or human driven vehicles are present. A two-level hierarchy is proposed: a high-level data-driven strategy predictor and a lower-level model-based feedback controller. The strategy predictor maps an encoding of a dynamic environment to a set of high-level strategies via a neural network. Depending on the selected strategy, a set of time-varying hyperplanes in the AV’s position space is generated online and the corresponding halfspace constraints are included in a lower-level model-based receding horizon controller. These strategy-dependent constraints drive the vehicle towards areas where it is likely to remain feasible. Moreover, the predicted strategy also informs switching between a discrete set of policies, which allows for more conservative behavior when prediction confidence is low. We demonstrate the effectiveness of the proposed data-driven hierarchical control framework in a two-car collision avoidance scenario through simulations and experiments on a 1/10 scale autonomous car platform where the strategy-guided approach outperforms a model predictive control baseline in both cases.

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IROS Conference 2021 Conference Paper

Learning Environment Constraints in Collaborative Robotics: A Decentralized Leader-Follower Approach

Monimoy Bujarbaruah
Yvonne R. Stürz
Conrad Holda
Karl Henrik Johansson
Francesco Borrelli

In this paper, we propose a leader-follower hierarchical strategy for two robots collaboratively transporting an object in a partially known environment with obstacles. Both robots sense the local surrounding environment and react to obstacles in their proximity. We consider no explicit communication, so the local environment information and the control actions are not shared between the robots. At any given time step, the leader solves a model predictive control (MPC) problem with its known set of obstacles and plans a feasible trajectory to complete the task. The follower estimates the inputs of the leader and uses a policy to assist the leader while reacting to obstacles in its proximity. The leader infers obstacles in the follower’s vicinity by using the difference between the predicted and the real-time estimated follower control action. A method to switch the leader-follower roles is used to improve the control performance in tight environments. The efficacy of our approach is demonstrated with detailed comparisons to two alternative strategies, where it achieves the highest success rate, while completing the task fastest.

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IROS Conference 2006 Conference Paper

Scanning the Environment with Two Independent Cameras - Biologically Motivated Approach

Ofir Avni
Francesco Borrelli
Gadi Katzir
Ehud Rivlin
Héctor Rotstein

In this paper we present a novel method for visual scanning and target tracking by means of independent pan-tilt cameras which mimic the chameleon visual system. We present a systematic and optimization-based approach to the problem, from the high-level to the low-level control. In particular, in the first part we develop a new algorithm for scanning the sphere using multiple cameras. The algorithm combines information about the environment and a model of target movement, to perform optimal scanning by means of stochastic dynamic programming. In the second part we develop a model-based control strategy for target tracking. A switching optimal control strategy based on smooth pursuit and saccades is designed by means of explicit model predictive control (MPC) theory. We simulated and experimentally validated our theory on a robotic chameleon head composed of two independent pan-tilt cameras. The resulting scanning pattern and target tracking has a remarkable resemblance to the one seen in nature by chameleons

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