L4DC 2026

106 papers

Central Limit Theorems for Asynchronous Averaged Q-Learning

Xingtu Liu

Enhancing Inverse Reinforcement Learning through Encoding Dynamic Information in Reward Shaping

Simon Sinong Zhan, Philip Wang, Qingyuan Wu, Ruochen Jiao, Yixuan Wang

Subgradient Method for System Identification with Non-Smooth Objectives

Baturalp Yalcin, Jihun Kim, Javad Lavaei

Online Subspace Learning on Flag Manifolds for System Identification

Dian Jin, Jeremy Coulson

Learning Nonholonomic Dynamics with Constraint Discovery

Baiyue Wang, Anthony Bloch

Constraint-Aware Reinforcement Learning via Adaptive Action Scaling

Murad Dawood, Usama Ahmed Siddiquie, Shahram Khorshidi, Maren Bennewitz

Foundations of Safe Online Reinforcement Learning in the Linear Quadratic Regulator: √T-Regret

Benjamin Schiffer, Lucas Janson

Learning to accelerate distributed ADMM using graph neural networks

Henri Doerks, Paul Häusner, Daniel Hernández Escobar, Jens Sjölund

Learning Multi-Robot Coordination with Invariant Consensus Stabilization

Hang Yin, Christos Verginis, Danica Kragic

Scalable Infinitesimal Generator–Based Koopman Learning for Long-Horizon Prediction

Minseok Jeong, Soojean Han, Hyo-Sang Shin

Misinformation Mitigation over Social Networks: a Control Approach

Nicolò Pagan, Andreas Philippou, Giulia De Pasquale

Can Optimal Transport Improve Federated Inverse Reinforcement Learning?

David Millard, Ali Baheri

Balance Equation-based Distributionally Robust Offline Imitation Learning

Rishabh Agrawal, Yusuf Alvi, Rahul Jain, Ashutosh Nayyar

Choose Wisely: Data-driven Predictive Control for Nonlinear Systems Using Online Data Selection

Joshua Näf, Keith Moffat, Jaap Eising, Florian Dorfler

Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes

Jan-Hendrik Ewering, Robin Erik Herrmann, Niklas Wahlström, Thomas B. Schön, Thomas Seel

Learning Quantized Continuous Controllers for Integer Hardware

Fabian Kresse, Christoph H. Lampert

Adversarially Robust Multitask Adaptive Control

Kasra Fallah, Leonardo Felipe Toso, James Anderson

Adaptive Policy Selection and Fine-Tuning under Interaction Budgets for Offline-to-Online Reinforcement Learning

Alper Kamil Bozkurt, Xiaoan Xu, Shangtong Zhang, Miroslav Pajic, Yuichi Motai

Koopman-BoxQP: Solving Large-Scale NMPC at kHz Rates

Liang Wu, Wallace Gian Yion Tan, Richard Braatz, Jan Drgona

Assumed Density Filtering and Smoothing with Neural Network Surrogate Models

Simon Kuang, Xinfan Lin

Instrumental variables system identification with L^p consistency

Simon Kuang, Xinfan Lin

On the Exponential Stability of Koopman Model Predictive Control

Xu Shang, Jorge Cortes, Yang Zheng

Embodied Learning of Reward for Musculoskeletal Control with Vision Language Models

Saraswati Soedarmadji, Yunyue Wei, Chen Zhang, Yisong Yue, Yanan Sui

Topological Dynamics via Learned Hybrid Systems

Bernardo Rivas, William Kalies, Kaito Iwasaki, Anthony Bloch, Maani Ghaffari

Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagation

Nikolaus Vertovec, Frederik Baymler Mathiesen, Thom Badings, Luca Laurenti, Alessandro Abate

Latent Linear Quadratic Regulator for Robotic Control Tasks

yuan zhang, Shaohui Yang, Toshiyuki Ohtsuka, Colin Jones, Joschka Boedecker

Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems

Hans Harder, Abhijeet Vishwasrao, Luca Guastoni, Ricardo Vinuesa, Sebastian Peitz

Online Tracking with Predictions for Nonlinear Systems with Koopman Linear Embedding

Chih-Fan Pai, Xu Shang, Jiachen Qian, Yang Zheng

CoFineLLM: Conformal Finetuning of LLMs for Language-Instructed Robot Planning

Jun Wang, Yevgeniy Vorobeychik, Yiannis Kantaros

Chebyshev polynomials meet Nevanlinna-Pick interpolation: An automated procedure for algorithm synthesis

Ibrahim Kurban Ozaslan, Tryphon Georgiou, Mihailo Jovanovic

Harnessing Data from Clustered LQR Systems: Personalized and Collaborative Policy Optimization

Vinay Kanakeri, Shivam Bajaj, Ashwin Verma, Vijay Gupta, Aritra Mitra

Online Learning and Coverage of Unknown Fields Using Random-Feature Gaussian Processes

Ruijie Du, Ruoyu Lin, Yanning Shen, Magnus B. Egerstedt

Deep QP Safety Filter: Model-free Learning for Reachability-based Safety Filter

Byeongjun Kim, H. Jin Kim

PG-BIG: Personalized Guidance for Biomechanically Informed Generative Models in Exercise Science

Nicholas C King, Jared Maeyama, Shubh Maheshwari, Andrew McCulloch, Rose Yu

Robust Verification of Controllers under State Uncertainty via Hamilton-Jacobi Reachability Analysis

Albert Lin, Alessandro Pinto, Somil Bansal

MATT-Diff: Multimodal Active Target Tracking by Diffusion Policy

Saida Liu, Nikolay Atanasov, Shumon Koga

PFEM-GP-dPHS : a finite element framework for combining Gaussian processes and infinite-dimensional port-Hamiltonian systems

Florian Courteville, Iain Henderson, Denis MATIGNON, Sylvain Dubreuil

ATOM-CBF: Adaptive Safe Perception-Based Control under Out-of-Distribution Measurements

Kai S. Yun, Navid Azizan

Stability of Certainty-Equivalent Adaptive LQR for Linear Systems with Unknown Time-Varying Parameters

Marcell Bartos, Johannes Köhler, Florian Dorfler, Melanie Zeilinger

Data-driven Acceleration of MPC with Guarantees

Agustin Castellano, PAN SHIJIE, Enrique Mallada

Physics-Informed Neural Operators for Cardiac Electrophysiology

Hannah Lydon, Milad Kazemi, Martin Bishop, Nicola Paoletti

Efficient State and Parameter Estimation of Nonlinear State-Space Models through Probabilistic Optimal Control

Victor Vantilborgh, Mohammad Mahmoudi Filabadi, Tom Lefebvre, Guillaume Crevecoeur

Provably Safe Stein Variational Clarity-Aware Informative Planning

Kaleb Ben Naveed, Utkrisht Sahai, Anouck Girard, Dimitra Panagou

Policy Optimization for Unknown Systems using Differentiable MPC

Riccardo Zuliani, Efe C. Balta, John Lygeros

Learned Incremental Nonlinear Dynamic Inversion for Quadrotors with and without Slung Payloads

Eckart Cobo-Briesewitz, Khaled Wahba, Wolfgang Hönig

A Robust Task-Level Control Architecture for Learned Dynamical Systems

Eshika Pathak, Ahmed Aboudonia, Sandeep Banik, NAIRA HOVAKIMYAN

The PID Controller Strikes Back: Classical Controller Helps Mitigate Barren Plateaus in Noisy Variational Quantum Circuits

Zhehao Yi, Rahul Bhadani

Formalizing Task-Space Complexity for Zero-Shot Generalization

Jung-Hoon Cho, Heling Zhang, Siqi Du, Roy Dong, Cathy Wu

Learning to Act Through Contact: A Unified View of Multi-Task Robot Learning

Shafeef Omar, Majid Khadiv

TIGER-MARL: Enhancing Multi-Agent Reinforcement Learning with Temporal Information through Graph-based Embeddings and Representations

Nikunj Gupta, Ludwika Twardecka, James Zachary Hare, Jesse Milzman, Rajgopal Kannan

Optimal control of the future via prospective learning with control

Yuxin Bai, Aranyak Acharyya, Ashwin De Silva, Zeyu Shen, James Hassett

Sparse-to-Field Reconstruction via Stochastic Neural Dynamic Mode Decomposition

Yujin Kim, Sarah Dean

WOMBET: World Model-based Experience Transfer for Robust and Sample-efficient Reinforcement Learning

Mintae Kim, Koushil Sreenath

Learning-Based Resilient Interval Observers for Nonlinear Discrete-Time Bounded-Error Systems

Mareddu Siva Rohit, Parisa Ansari Bonab, Elisabeth Andarge Gedefaw, Mohammad Khajenejad

On the Convergence of Overparameterized Problems: Inherent Properties of the Compositional Structure of Neural Networks

Arthur Castello Branco de Oliveira, Dhruv D. Jatkar, Eduardo Sontag

Hierarchical End-to-End Taylor Bounds for Complete Neural Network Verification

Taha Entesari, Mahyar Fazlyab

A Hybrid Learning-to-Optimize Framework for Mixed-Integer Quadratic Programming

Viet-Anh Le, Mu Xie, Rahul Mangharam

Differentiable Filtering for Learning Hidden Markov Models

Reginald Zhiyan Chen, Heng-Sheng Chang, Prashant G Mehta

An accelerated proximal bundle method for convex optimization

Feng-Yi Liao, Thomas Madden, Yang Zheng

OffRIPP: Offline RL-based Informative Path Planning

Srikar Babu Gadipudi, Srujan Deolasee, Siva Kailas, Wenhao Luo, Katia P. Sycara

Certified Robust Invariant Polytope Training in Neural Controlled ODEs

Akash Harapanahalli, Samuel Coogan

BURNS: Backward Underapproximate Reachability for Neural-Feedback-Loop Systems

Chelsea Rose Sidrane, Jana Tumova

Optimizing Coordination among Bounded Rational Agents

Zhewei Wang, Marcos M. Vasconcelos

Online Caching in Tree Networks: Algorithms, Regret, and Complexity

Ativ Joshi, Rajat De, Rajarshi Bhattacharjee, Cameron N Musco, Abhishek Sinha

Offline Reinforcement Learning for Rotation Profile Control in Tokamaks

Rohit Sonker, Hiro Josep Farre Kaga, Jiayu Chen, Andrew Rothstein, Ian Char

Online Adaptive Probabilistic Safety Certificate with Language Guidance

Zhuoyuan Wang, Xiyu Deng, Hikaru Hoshino, Yorie Nakahira

Distributionally Robust Regret Optimal Control Under Moment-Based Ambiguity Sets

Feras Al Taha, Eilyan Bitar

Safe Control using Learned Safety Filters and Adaptive Conformal Inference

Sacha Huriot, Ihab Tabbara, Hussein Sibai

Workflow Search Reinforcement Learning over Structured Decompositions

Guangyu Jiang, Shu Hong, Mahdi Imani, Nathaniel D. Bastian, Tian Lan

Certified Training with Branch-and-Bound for Lyapunov-stable Neural Control

Zhouxing Shi, Haoyu Li, Cho-jui Hsieh, Huan Zhang

Learning to Plan, Planning to Learn: Adaptive Hierarchical RL-MPC for Sample-Efficient Decision Making

Toshiaki Hori, Jonathan DeCastro, Deepak Edakkattil Gopinath, Avinash Balachandran, Guy Rosman

Verifying Nonlinear Neural Feedback Systems using Polyhedral Enclosures

Samuel I. Akinwande, Chelsea Rose Sidrane, Mykel Kochenderfer, Clark Barrett

TD-M(PC)$^2$: Improving Temporal Difference MPC Through Policy Constraint

Haotian Lin, Pengcheng Wang, Jeff Schneider, Guanya Shi

Safe Planning in Interactive Environments via Iterative Policy Updates and Adversarially Robust Conformal Prediction

Omid Mirzaeedodangeh, Eliot Seo Shekhtman, Nikolai Matni, Lars Lindemann

Warm-starting active-set solvers using graph neural networks

Ella J. Schmidtobreick, Daniel Arnström, Paul Häusner, Jens Sjölund

BarrierBench: Evaluating Large Language Models for Safety Verification in Dynamical Systems

Ali Taheri, Alireza Taban, Sadegh Soudjani, Ashutosh Trivedi

Improving EV Aggregate Flexibility with End-to-End Learning

Apoorva Thanvantri, Christopher Yeh, Nicolas Christianson, Adam Wierman

When Environments Shift: Safe Planning with Generative Priors and Robust Conformal Prediction

Kaizer Rahaman, Jyotirmoy V. Deshmukh, Ashish R. Hota, Lars Lindemann

Trajectory-Level Experimental Design for Fast Safety Parameter Estimation of Unknown Environments by Autonomous Systems

Aneesh Raghavan, Karl Henrik Johansson

GCImOpt: Learning efficient goal-conditioned policies by imitating optimal trajectories

Jon Goikoetxea, Jesús F. Palacián

Fourier Weak SINDy: Spectral Test Function Selection for Robust Model Identification

Zhiheng Chen, Urban Fasel, Anastasia Bizyaeva

Active Constraint Learning in High Dimensions from Demonstrations

Zheng Qiu, Chih-Yuan Chiu, Glen Chou

Robust Least-Squares Optimization for Data-Driven Predictive Control: A Geometric Approach

Shreyas Bharadwaj, Bamdev Mishra, Cyrus Mostajeran, Alberto Padoan, Jeremy Coulson

Precise Performance of Linear Denoisers in The Proportional Regime

Reza Ghane, Danil Akhtiamov, Babak Hassibi

Convergence of Vector Quantization–Based Classifiers to the Bayes Optimal Classifier with Applications to Hybrid System Identification

Aneesh Raghavan, Christos Mavridis, Karl Henrik Johansson, John Baras

Realistic Internal Dynamics Are Essential for Human-Like Control: An Optimal Feedback Control Perspective

Nima Akbari

HALO: Hybrid Auto-encoded Locomotion with Learned Latent Dynamics, Poincaré Maps, and Regions of Attraction

Blake Werner, Sergio Esteban, Massimiliano de Sa, Max H. Cohen, Aaron Ames

Safety Beyond the Training Data: Robust Out-of-Distribution MPC via Conformalized System Level Synthesis

Anutam Srinivasan, Antoine Leeman, Glen Chou

BGCL:Learning Constitutive Laws for System Identification

Abhishek Patkar, Kamal Youcef-Toumi

A Unified Framework for Locality in Scalable MARL

Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni, Lijun Chen

Kernel-Based Safe Exploration in Deep Reinforcement Learning

R Majumdar, Nikhil Singh, Sadegh Soudjani

Near Optimal Convergence to Coarse Correlated Equilibrium in General-Sum Markov Games

Asrin Efe Yorulmaz, Tamer Basar

Zero-Shot Function Encoder-Based Differentiable Predictive Control

Hassan Iqbal, Xingjian Li, Tyler Ingebrand, Adam Thorpe, Krishna Kumar

Koopman Operator for Stability Analysis: Theory with a Linear–Radial Product Reproducing Kernel

Wentao Tang, Xiuzhen Ye

FALCON: Learning Force-Adaptive Humanoid Loco-Manipulation

Yuanhang Zhang, Yifu Yuan, Prajwal Gurunath, Ishita Gupta, Shayegan Omidshafiei

ACE: Adapting sampling for Counterfactual Explanations

Margarita A. Guerrero, Cristian R. Rojas

Learning to Solve Constrained Bilevel Control Co-Design Problems

James Kotary, Himanshu Sharma, Ethan King, Draguna L Vrabie, Ferdinando Fioretto

ECO: Energy-Constrained Operator Learning for Chaotic Dynamics with Boundedness Guarantees

Andrea Goertzen, Sunbochen Tang, Navid Azizan

DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning

Srujan Deolasee, Siva Kailas, Wenhao Luo, Katia P. Sycara, Woojun Kim

Time-aware Motion Planning in Dynamic Environments with Conformal Prediction

Kaier Liang, Licheng Luo, Yixuan Wang, Mingyu Cai, Cristian Ioan Vasile