Oral Session 1: Safe Learning, Reachability, and Safety Filters

• Kernel-Based Safe Exploration in Deep Reinforcement Learning
  Rupak Majumdar, Nikhil Singh, Sadegh Soudjani

• Time-aware Motion Planning in Dynamic Environments with Conformal Prediction
  Kaier Liang, Licheng Luo, Yixuan Wang, Mingyu Cai, Cristian Ioan Vasile

• How to Train Your Latent Control Barrier Function: Smooth Safety Filtering Under Hard-to-Model Constraints
  Kensuke Nakamura, Arun L Bishop, Steven Man, Aaron M. Johnson, Zachary Manchester, Andrea Bajcsy

• Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guarantees
  Devesh Nath, Haoran Yin, Glen Chou

Oral Session 2: Reinforcement Learning for Robotics and Planning

• FALCON: Learning Force-Adaptive Humanoid Loco-Manipulation
  Yuanhang Zhang, Yifu Yuan, Prajwal Gurunath, Ishita Gupta, Shayegan Omidshafiei, Ali-akbar Agha-mohammadi, Marcell Vazquez-Chanlatte, Liam Pedersen, Tairan He, Guanya Shi

• DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning
  Srujan Deolasee, Siva Kailas, Wenhao Luo, Katia P. Sycara, Woojun Kim

• Adapting World Models with Latent-State Dynamics Residuals
  JB Lanier, Kyungmin Kim, Armin Karamzade, Yifei Liu, Ankita sinha, Kathleen He, Davide Corsi, Roy Fox

• Model-Based Reinforcement Learning under Random Observation Delays
  Armin Karamzade, Kyungmin Kim, JB Lanier, Davide Corsi, Roy Fox

Oral Session 3: Learning Dynamical Models and System Identification

• Zero-Shot Function Encoder-Based Differentiable Predictive Control

Hassan Iqbal, Xingjian Li, Tyler Ingebrand, Adam Thorpe, Krishna Kumar, Ufuk Topcu, Jan Drgona

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

Wentao Tang, Xiuzhen Ye

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

Andrea Goertzen, Sunbochen Tang, Navid Azizan

• CableRobotGraphSim: A Graph Neural Network for Modeling Partially Observable Cable-Driven Robot Dynamics

Nelson Chen, William R. Johnson III, Rebecca Kramer-Bottiglio, Kostas Bekris, Mridul Aanjaneya

Oral Session 4: Learning Theory, Optimization, and Decision-Making

• Near Optimal Convergence to Coarse Correlated Equilibrium in General-Sum Markov Games
  Asrin Efe Yorulmaz

• 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, Jan Drgona

• Flickering Multi-Armed Bandits

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

Poster Session 1

• Kernel-Based Safe Exploration in Deep Reinforcement Learning

R Majumdar, Nikhil Singh, Sadegh Soudjani

• Time-aware Motion Planning in Dynamic Environments with Conformal Prediction

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

• How to Train Your Latent Control Barrier Function: Smooth Safety Filtering Under Hard-to-Model Constraints

Kensuke Nakamura, Arun L Bishop, Steven Man, Aaron M. Johnson, Zachary Manchester, Andrea Bajcsy

• Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guarantees

Devesh Nath, Haoran Yin, Glen Chou

• Physics-Informed Neural Operators for Cardiac Electrophysiology

Hannah Lydon, Milad Kazemi, Martin Bishop, Nicola Paoletti

• Central Limit Theorems for Asynchronous Averaged Q-Learning

Xingtu Liu

• Learning to Control Misinformation: A Closed-loop Approach for Misinformation Mitigation over Social Networks

Nicolò Pagan, Andreas Philippou, Giulia De Pasquale

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

Viet-Anh Le, Mu Xie, Rahul Mangharam

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

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

• Warm-starting active-set solvers using graph neural networks

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

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

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

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

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

• Constraint-Aware Reinforcement Learning via Adaptive Action Scaling

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

• A Robust Task-Level Control Architecture for Learned Dynamical Systems

Eshika Pathak, Ahmed Aboudonia, Sandeep Banik, Naira Hovakimyan

• Online Tracking with Predictions for Nonlinear Systems with Koopman Linear Embedding

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

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

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

• 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

• Foundations of Safe Online Reinforcement Learning in the Linear Quadratic Regulator: $\sqrt{T}$-Regret

Benjamin Schiffer, Lucas Janson

• An accelerated proximal bundle method for convex optimization

Feng-Yi Liao, Thomas Madden, Yang Zheng

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

Zhehao Yi, Rahul Bhadani

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

Zhiheng Chen, Urban Fasel, Anastasia Bizyaeva

• Certified Robust Invariant Polytope Training in Neural Controlled ODEs

Akash Harapanahalli, Samuel Coogan

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

Ibrahim Kurban Ozaslan, Tryphon Georgiou, Mihailo Jovanovic

• Subgradient Method for System Identification with Non-Smooth Objectives

Baturalp Yalcin, Jihun Kim, Javad Lavaei

• Precise Performance of Linear Denoisers in The Proportional Regime

Reza Ghane, Danil Akhtiamov, Babak Hassibi

• Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes

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

• Learning-Augmented Stochastic MPC for Battery Control under Forecast Uncertainty

Muhammad Amad Asif, Nathan Dahlin

• World Model Predictive Safety Filtering

Madison Bland, Jaime Fisac, Albert Lin, Somil Bansal

• Operator-Theoretic Foundations and Policy Gradient Methods for General MDPs

Abhishek Gupta, Aditya Mahajan

• SafeDPMSolver: A Constraint-Aware Framework for Safe Sampling

Sugheerth Sreedharan, Karthik Dantu

• SO(3)-Calibrated Point-Cloud Goal Metrics for Dexterous Reorientation

Yashom Dighe, Karthik Dantu

• Learning Safe Control with Barrier-Supervised Transformers

Anandsingh Chauhan, Kunal Garg

• AdaFair-MARL: Enforcing Adaptive Fairness Constraints in Multi-Agent Reinforcement Learning

Promise Ekpo, Saesha Agarwal, Felix Grimm, Jiachang Liu, Lekan Molu, Angelique Taylor

• LMI-Net: Linear Matrix Inequality–Constrained Neural Networks via Differentiable Projection Layers

Sunbochen Tang, Andrea Goertzen, Navid Azizan

• Consensus Under Noise: A Unified Analysis of Social Learning in Discrete and Continuous Belief Spaces

Sai Niranjan Ramachandran, Pranav Chakravarthy Bommarasipetta

• CLT-Optimal Parameter Error Bounds for Linear System Identification

Yichen Zhou, Stephen Tu

• Value Function Decomposition for Temporal Logic

William Sharpless, Dylan Hirsch, Oswin So, Sander Tonkens, Nikhil Shinde, Chuchu Fan, Sylvia Herbert

• Greedy Algorithms Beyond Matroids: Oracle-Constrained Greedoids for Alignment in Cyber-Physical Systems

Joan Vendrell Gallart, Russell Bent, Solmaz Kia

• 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

Poster Session 2

• FALCON: Learning Force-Adaptive Humanoid Loco-Manipulation

Yuanhang Zhang, Yifu Yuan, Prajwal Gurunath, Ishita Gupta, Shayegan Omidshafiei, Ali-akbar Agha-mohammadi, Marcell Vazquez-Chanlatte, Liam Pedersen, Tairan He, Guanya Shi

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

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

• Adapting World Models with Latent-State Dynamics Residuals

JB Lanier, Kyungmin Kim, Armin Karamzade, Yifei Liu, Ankita sinha, Kathleen He, Davide Corsi, Roy Fox

• Model-Based Reinforcement Learning under Random Observation Delays

Armin Karamzade, Kyungmin Kim, JB Lanier, Davide Corsi, Roy Fox

• Embodied Learning of Reward for Musculoskeletal Control with Vision Language Models

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

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

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

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

Eckart Cobo-Briesewitz, Khaled Wahba, Wolfgang Hönig

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

Anutam Srinivasan, Glen Chou

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

Jun Wang, Yevgeniy Vorobeychik, Yiannis Kantaros

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

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

• Can Optimal Transport Improve Federated Inverse Reinforcement Learning?

David Millard, Ali Baheri

• Improving EV Aggregate Flexibility with End-to-End Learning

Apoorva Thanvantri, Christopher Yeh, Nicolas Christianson, Adam Wierman

• Provably Safe Stein Variational Clarity-Aware Informative Planning

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

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

Albert Lin, Alessandro Pinto, Somil Bansal

• Assumed Density Filtering and Smoothing with Neural Network Surrogate Models

Simon Kuang, Xinfan Lin

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

Joshua Näf, Keith Moffat, Jaap Eising, Florian Dorfler

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

Marcell Bartos, Johannes Köhler, Florian Dorfler, Melanie Zeilinger

• Online Adaptive Probabilistic Safety Certificate with Language Guidance

Zhuoyuan Wang, Xiyu Deng, Hikaru Hoshino, Yorie Nakahira

• 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

• Optimizing Coordination among Bounded Rational Agents

Zhewei Wang, Marcos M. Vasconcelos

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

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

• Belief Net: A Filter-Based Framework for Learning Hidden Markov Models from Observations

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

• Offline Reinforcement Learning for Rotation Profile Control in Tokamaks

Rohit Sonker, Hiro Josep Farre Kaga, Jiayu Chen, Andrew Rothstein, Ian Char, Ricardo Shousha, Egemen Kolemen, Jeff Schneider

• Enhancing Inverse Reinforcement Learning through Encoding Dynamic Information in Reward Shaping

Simon Sinong Zhan, Philip Wang, Qingyuan Wu, Ruochen Jiao, Yixuan Wang, Chao Huang, Qi Zhu

• Topological Dynamics via Learned Hybrid Systems

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

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

Yujin Kim, Sarah Dean

• Differentiable Reinforcement Learning for Path Tracking by an Agile Fish-Like Robot

V.R.R Varikuti, Kartik Loya, Prasanth Chivikula

• Pick-to-Learn: Tightness and Scalability

Dario Paccagnan

• Information-theoretic receding-horizon active learning of nonlinear dynamical systems

Juncal Arbelaiz, Anushri Arora, Jonathan W. Pillow

• Convergence of Natural Policy Gradient Primal-Dual Methods for Constrained Convex MDPs

Dongsheng Ding

• Learning human driver dynamics from experiments on virtual rings

Bence Szaksz, Xunbi A. Ji, Tamas G. Molnar, Sergei S. Avedisov, Gábor Stepan, Gábor Orosz

• Diffusion-Based Trajectory Planning for Excavators with Learned Dynamics Models

Sugheerth Sreedharan, Christo Aluckal, Pavan Yarlagadda, Ganesh Prabakaran, Rugved Raote, Shouvik Das, Karthik Dantu

• A Multimodal Architecture for Video System Identification: Visual Imagination via Grey-Box Physical Grounded Simulations

Antonio Álvarez-López, Daniel Fernández, Daniel López, Helon Vicente Hultmann Ayala

• Accelerated system identification with differentiable neural model reduction

Nathan M. Urban

• FNO∠θ: Extended Fourier neural operator for learning state and optimal control of distributed parameter systems

Zhexian Li, Ketan Savla

• Locally Stable Neural ODEs with Characterized Region of Attraction

Alice Harting, Karl Henrik Johansson, Sophie Tarbouriech, Matthieu Barreau

• Scalar Federated Learning for Linear Quadratic Regulator

Mohammadreza Rostami, Shahriar Talebi, Solmaz S. Kia

Poster Session 3

• Zero-Shot Function Encoder-Based Differentiable Predictive Control

Hassan Iqbal, Xingjian Li, Tyler Ingebrand, Adam Thorpe, Krishna Kumar, ufuk topcu, Jan Drgona

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

Wentao Tang, Xiuzhen Ye

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

Andrea Goertzen, Sunbochen Tang, Navid Azizan

• CableRobotGraphSim: A Graph Neural Network for Modeling Partially Observable Cable-Driven Robot Dynamics

Nelson Chen, William R. Johnson III, Rebecca Kramer-Bottiglio, Kostas Bekris, Mridul Aanjaneya

• LoCReach: Reachability analysis using the Lipschitz of the Curvature

Taha Entesari, Mahyar Fazlyab

• Data-driven Acceleration of MPC with Guarantees

Agustin Castellano, PAN SHIJIE, Enrique Mallada

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

Shreyas Bharadwaj, Bamdev Mishra, Cyrus Mostajeran, Alberto Padoan, Jeremy Coulson, Ravi N. Banavar

• Balance Equation-based Distributionally Robust Offline Imitation Learning

Rishabh Agrawal, Yusuf Alvi, Rahul Jain, Ashutosh Nayyar

• 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, Viktor Prasanna

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

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

• Safe Control using Learned Safety Filters and Adaptive Conformal Inference

Sacha Huriot, Ihab Tabbara, Hussein Sibai

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

Minseok Jeong, SooJean Han, Hyo-Sang Shin

• Formalizing Task-Space Complexity for Zero-Shot Generalization

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

• Learning to accelerate distributed ADMM using graph neural networks

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

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

Nima Akbari

• Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagation

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

• Learning Multi-Robot Coordination with Invariant Consensus Stabilization

Hang Yin, Christos Verginis, Danica Kragic

• Policy Optimization for Unknown Systems using Differentiable MPC

Riccardo Zuliani, Efe C. Balta, John Lygeros

• A Unified Framework for Locality in Scalable MARL

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

• OffRIPP: Offline RL-based Informative Path Planning

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

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

Byeongjun Kim, H. Jin Kim

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

Aneesh Raghavan, Karl Henrik Johansson

• MoET: Model-based Experience Transfer for Robust and Sample-efficient Reinforcement Learning

Mintae Kim, Koushil Sreenath

• 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

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

Chelsea Rose Sidrane, Jana Tumova

• Costate-based Policy Learning for Real-Time Optimal Control

Isaiah A. Agboola, Yuxin Tong, Uduak Inyang-Udoh

• Global Convergence of Policy Gradient Methods for ReLU Controllers in Linear Quadratic Regulation

Jhojan A. Rodriguez-Gil, César A. Uribe

• Learning Invariant Visual Representations for Planning with Joint-Embedding Predictive World Models

Leonardo F. Toso, Davit Shadunts, Yunyang Lu, Nihal Sharma, Donglin Zhan, Nam H. Nguyen, James Anderson

• A Bregman Divergence Approach for Tracking of Linear Systems with Convex Costs

Joudi Hajar, Soon-Jo Chung, Fred Y. Hadaegh, Babak Hassibi

• From Probing to Prognosis: Diagnosing Neural Networks through Internal Representations

Hakka Madan, Anandsingh Chauhan, Kunal Garg

• Real-Time Motion Planning using Vision Language Models

Devika Shaj Kumar Nair, Kunal Garg

• RL Policies Are Globally Smooth: A Structural Insight from Synthetic Probing

Tanmay Ambadkar, Abhinav Verma

• Conformal Prediction Regions for Continuous-Time Trajectories under Random Sampling

Joaquin Alvarez, Matteo Sesia, Jyotirmoy V. Deshmukh, Lars Lindemann

• Adaptive Koopman Learning for Off-Road Vehicle Dynamics on Deformable Terrain

Kartik Loya, Phanindra Tallapragada

• Stable Zero Dynamics in Latent Dynamics Learning

Paul Lutkus, Kaiyuan Wang, Lars Lindemann, Stephen Tu

• Submodular Welfare under Routing Coupling: A Hierarchical Decomposition with Perturbation Guarantees

Joan Vendrell Gallart, Nhat-Minh Tang-Nguyen, Alan Kuhnle, Solmaz Kia

• Are Control Density Functions Practical: Density vs Barrier Functions

Grant Pauker, Stephen Tu, Lars Lindemann

• Incremental stability in p = 1 and p = ∞: classification and synthesis

Simon Kuang, Xinfan Lin

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

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

Poster Session 4

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

Asrin Efe Yorulmaz

• 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, Jan Drgona

• Flickering Multi-Armed Bandits

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

• Verifying Nonlinear Neural Feedback Systems using Polyhedral Enclosures

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

• Distributionally Robust Regret Optimal Control Under Moment-Based Ambiguity Sets

Feras Al Taha, Eilyan Bitar

• MATT-Diff: Multimodal Active Target Tracking by Diffusion Policy

Saida Liu, Nikolay Atanasov, Shumon Koga

• Learning Nonholonomic Dynamics with Constraint Discovery

Baiyue Wang, Anthony Bloch

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

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

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

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

• 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

• Adversarially Robust Multitask Adaptive Control

Kasra Fallah, Leonardo Felipe Toso, James Anderson

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

Jon Goikoetxea, Jesús F. Palacián

• Latent Linear Quadratic Regulator for Robotic Control Tasks

Yuan Zhang, Shaohui Yang, Toshiyuki Ohtsuka, Colin Jones, Joschka Boedecker

• Online Subspace Learning on Flag Manifolds for System Identification

Dian Jin, Jeremy Coulson

• BGCL:Learning Constitutive Laws for System Identification

Abhishek Patkar, KAMAL YOUCEF-TOUMI

• Active Constraint Learning in High Dimensions from Demonstrations

Zheng Qiu, Chih-Yuan Chiu, Glen Chou

• Optimal Control of the Future via Prospective Foraging

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

• Learning Quantized Continuous Controllers for Integer Hardware

Fabian Kresse, Christoph H. Lampert

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

Shafeef Omar, Majid Khadiv

• 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

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

Kai S. Yun, Navid Azizan

• On the Exponential Stability of Koopman Model Predictive Control

Xu Shang, Jorge Cortes, Yang Zheng

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

Ali Taheri, Alireza Taban, Sadegh Soudjani, Ashutosh Trivedi

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

Haotian Lin, Pengcheng Wang, Jeff Schneider, Guanya Shi

• Scalable Implicit Graph Neural Networks via Contractivity and Parameterizations

Anand Gokhale, Yu Kawano, Anton V Proskurnikov, Francesco Bullo

• Learning Structure-Preserving Perturbations to Potential Energy Fields from Images

Harshit Agarwal, Harsh Sharma

• Learning Kalman Policy for Singular Unknown Noise Covariances

Larsen Bier, Shahriar Talebi

• Strategically robust game theory: foundations and new developments

N. Lanzetti, S. Fricker, S. Bolognani, F. Dörfler, D. Paccagnan

• Sampling-Horizon Neural Operator Predictors for Nonlinear Control under Delayed Inputs

Luke Bhan, Miroslav Krstic, Yuanyuan Shi

• Parameter Synthesis for Continuous-Time Nonlinear Systems using Learning with Signal Temporal Logic

Alex Beaudin, Hanna Krasowski, Eric Palanques-Tost, Calin Belta, Murat Arcak

• Learning to Identify Basis Constraints: A Machine-Learning-Accelerated Clarkson Algorithm

Han Xu, Richard Chen, Yiheng Xie

• Provably Safe Residual Reinforcement Learning Using Tube MPC

Senne Bogaerts, Flavia Sofia Acerbo, Patrick Scheffe, Jan Swevers, Wilm Decré

• Who Should Yield? A Resource Allocation Framework for Fairness in Decentralized Multi-Agent Systems

Xiaoyang Cao, Zhe Fu, Jingqi Li, Alexandre M. Bayen

• Online Control with Energy Harvesting Constraints

Kamiar Asgari, Michael J. Neely

• Robust Peak-cost Constrained Reinforcement Learning

Shilpa Mukhopadhyay, Sourav Ganguly, Santosh Mohan Rajkumar, Honghao Wei, Debdipta Goswami, Arnob Ghosh

• Learning Explicit Structure of Dynamical Systems

Erik Arne Mathiesen-Dreyfus

• Instrumental variables system identification with $L^p$ consistency

Simon Kuang, Xinfan Lin