Linear Quadratic Regulator

Also known as LQR

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Latest papers 23

1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Square Root Gauss-Newton iLQR
Sep 17, 2026Maximilian Haas-Heger, Jur van den BergTrajectory Optimization Linear Quadratic Regulator
Waabi Innovation Inc.
Closing the Loop on the Poppy Humanoid: Bipedal Locomotion with Linear-Quadratic Control and Learned Cost Functions
Aug 27, 2026Xulin Chen, Borui He, Ruipeng Liu +3Humanoid Robot Locomotion Robotic Control
Syracuse University, Syracuse NY 13244, USA
Wasserstein Policy Gradient for Entropy-Regularized Linear-Quadratic Control
Aug 7, 2026Zhaoyu Zhu, Rui Gao, Shuang LiMaximum Entropy RL Policy Gradient Methods
University of Illinois Urbana-Champaign · The University of Texas at Austin · The Chinese University of Hong Kong, Shenzhen
Rake-Compress Riccati Recursions for Parallel Scenario-Tree Model Predictive Control
Aug 2, 2026João Sousa-PintoModel Predictive Control Linear Quadratic Regulator
independent researcher
Steering Robustness into World Action Models via Mechanistic Interpretability and Optimal Control
Jul 16, 2026Jihoon Hong, Julian Skifstad, Qiyue Dai +2World Models for Robotics Action-Conditioned World Models
Georgia Institute of Technology
Parallel Dynamic Programming for Conic Linear Quadratic Control
Jun 23, 2026Luyao Zhang, Gabriel Bravo-Palacios, Brian Plancher +1Dynamic Programming Feedback Control
Delft Center for Systems and Control, TU Delft, The Netherlands · Barnard College, Columbia University, New York, NY 10027 USA · & Dartmouth College, Hanover, NH 03755, USA
Horizon-Uniform Sensitivity Certificates for Finite-Horizon Pontryagin Systems
Jun 16, 2026Pyuyi Chufeng Huang, Zikang Song, Xingshu ChenDynamical Systems Feedback Control
School of Cyber Science and Engineering, Sichuan University, Chengdu, Sichuan, China · School of Mathematics, Sichuan University, Chengdu, Sichuan, China
Activation Steering of Video Generation Models via Reduced-Order Linear Optimal Control
Jun 3, 2026Jihoon Hong, Alice Chan, Qiyue Dai +2Text-to-Video Generation Video Generation
Georgia Institute of Technology Atlanta, GA 30308
When Mean Age Is Not Enough: Distribution-Aware Scheduling for Networked LQR Control
Jun 3, 2026Abdullah Y. Etcibasi, C. Emre Koksal, Eylem EkiciAge of Information Feedback Control
Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA.
SeaVis: Modeling and Control of a Remotely Operated Towed Vehicle for Seabed Visualization and Mapping
May 14, 2026Abdelhakim Amer, Aske Alstrup, Frederik Rasmussen +3Robotic Control Robust Control
Artificial Intelligence in Robotics Laboratory (AiR Lab), Department of Electrical and Computer Engineering, Aarhus University, 8000 Aarhus C, Denmark · EIVA a/s, 8660 Skanderborg, Denmark · Automatic Control Group (RAT), Department of Electrical Engineering and Information Technology, Paderborn University, Paderborn, Germany
Layerwise LQR for Geometry-Aware Optimization of Deep Networks
May 5, 2026Simon Dufort-Labbé, Pierre-Luc Bacon, Razvan Pascanu +2Deep Learning Optimization Second-Order Optimization
Mila, Université de Montréal · Samsung – SAIL Montreal
Rate-Optimal Regret for the Safe Learning-based Control of the Constrained Linear Quadratic Regulator
Apr 24, 2026Spencer Hutchinson, Nanfei Jiang, Mahnoosh AlizadehStochastic Linear Dynamical Systems Stochastic Optimal Control
Department of Electrical and Computer Engineering, University of California Santa Barbara
Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control
Apr 21, 2026Julian Skifstad, Xinyue Annie Yang, Glen ChouLLM Alignment Language Model-Based Control
1Georgia Institute of Technology, Atlanta, GA, USA. Schools of Electrical and Computer Engineering · 2Computer Science · 3Cybersecurity and Privacy, Aerospace Engineering
Learning the Riccati solution operator for time-varying LQR via Deep Operator Networks
Apr 20, 2026Jun Chen, Umberto Biccari, Junmin WangFeedback Control Deep Operator Networks
FNO $^{\angle θ}$ : Extended Fourier neural operator for learning state and optimal control of distributed parameter systems
Apr 6, 2026Zhexian Li, Ketan SavlaFourier Neural Operator PDE Operator Learning
Sonny Astani Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA 90089, USA. · Sonny Astani Department of Civil and Environmental Engineering, the Daniel Epstein Department of Industrial and Systems Engineering, and the Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA.
Scalar Federated Learning for Linear Quadratic Regulator
Apr 6, 2026Mohammadreza Rostami, Shahriar Talebi, Solmaz S. KiaFederated Learning Linear Quadratic Regulator
University of California, Irvine, CA, US · University of California, Los Angeles, CA, US
A Novel Hybrid PID-LQR Controller for Sit-To-Stand Assistance Using a CAD-Integrated Simscape Multibody Lower Limb Exoskeleton
Apr 4, 2026Ranjeet Kumbhar, Rajmeet Singh, Appaso M Gadade +2Assistive Robotics Linear Quadratic Regulator
Department of Mechanical Engineering Thapar Institute of Engineering and Technology Patiala, India · Khalifa University Center for Autonomous Robotic Systems (KUCARS) Khalifa University Abu Dhabi, UAE
Optimistic Online LQR via Intrinsic Rewards
Mar 30, 2026Marcell Bartos, Bruce D. Lee, Lenart Treven +3Linear Quadratic Regulator Regret Minimization in RL
ETH Zürich, Zürich, Switzerland
Separation is Optimal for LQR under Intermittent Feedback
Mar 29, 2026Abdullah Y. Etcibasi, C. Emre Koksal, Eylem EkiciSequential Decision Making Stochastic Optimal Control
The Ohio State University
A Schrödinger Eigenfunction Method for Long-Horizon Stochastic Optimal Control
Mar 24, 2026Louis Claeys, Artur Goldman, Zebang Shen +1Stochastic Optimal Control Linear Quadratic Regulator
ETH Zürich, Department of Mathematics · ETH Zürich, ETH AI Center, Institute for Machine Learning · ETH Zürich, Institute for Machine Learning
Douglas-Rachford Splitting for Group-Sparse Feedback Linear-Quadratic Control
Jul 26, 2025Lechen Feng, Xun Li, Yuan-Hua NiDistributed Optimization Nonconvex Optimization
Department of Applied Mathematics, The Hong Kong Polytechnic University, China · College of Artificial Intelligence, Nankai University, Tianjin, China
Tube-Based Robust Control Strategy for Vision-Guided Autonomous Vehicles
Mar 24, 2025Der-Hau LeeAutonomous Driving Robust Control
Department of Electrophysics, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
Sample Complexity of Linear Quadratic Regulator Without Initial Stability
Feb 20, 2025Amirreza Neshaei Moghaddam, Alex Olshevsky, Bahman GharesifardRL Control Sample Complexity

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Square Root Gauss-Newton iLQR

Maximilian Haas-Heger, Jur van den Berg

Waabi Innovation Inc.

Trajectory Optimization Linear Quadratic Regulator

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The iterative Linear Quadratic Regulator (iLQR) is a widely used algorithm for nonlinear trajectory optimization. At each iteration, it solves a local linear-quadratic approximation of the problem via dynamic programming, propagating a quadratic cost-to-go function. If the Hessian of the cost-to-go approximation is positive-semidefinite, one can derive a square root formulation of iLQR that propagates its Cholesky factor instead. This offers significant numerical advantages - much as square root Kalman filters improve upon their conventional counterparts - particularly when iLQR is used within an augmented Lagrangian framework for handling constraints, where large penalties degrade conditioning. Previous square root formulations of iLQR and related algorithms exist, but they are either numerically suboptimal, algorithmically complex, or both. In this paper, we show that the key to an effective square root formulation lies in the Gauss-Newton (weighted least-squares) structure of the cost function: this yields a positive semidefiniteness property that extends beyond the Hessian to the full augmented cost-to-go matrix, and enables a backward pass of remarkable simplicity in which each step reduces to a single QR-decomposition, from which the feedback gain and propagated Cholesky factor are extracted directly.