L4DC 2021

104 papers

A Data Driven, Convex Optimization Approach to Learning Koopman Operators Mario Sznaier

A New Objective for Identification of Partially Observed Linear Time-Invariant Dynamical Systems from Input-Output Data Nicholas Galioto, Alex Arkady Gorodetsky

A Unified Framework for Hamiltonian Deep Neural Networks Clara Lucía Galimberti, Liang Xu, Giancarlo Ferrari Trecate

Abstraction-Based Branch and Bound Approach to Q-Learning for Hybrid Optimal Control Benoît Legat, Raphaël M. Jungers, Jean Bouchat

Accelerated Concurrent Learning Algorithms via Data-Driven Hybrid Dynamics and Nonsmooth ODEs Daniel E. Ochoa, Jorge I. Poveda, Anantharam Subbaraman, Gerd S. Schmidt, Farshad R. Pour-Safaei

Accelerated Learning with Robustness to Adversarial Regressors Joseph E. Gaudio, Anuradha M. Annaswamy, José M. Moreu, Michael A. Bolender, Travis E. Gibson

Accelerating Distributed SGD for Linear Regression Using Iterative Pre-Conditioning Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra

Adaptive Risk Sensitive Model Predictive Control with Stochastic Search Ziyi Wang, Oswin So, Keuntaek Lee, Evangelos A. Theodorou

Adaptive Sampling for Estimating Distributions: A Bayesian Upper Confidence Bound Approach Dhruva Kartik, Neeraj Sood, Urbashi Mitra, Tara Javidi

Analysis of the Optimization Landscape of Linear Quadratic Gaussian (LQG) Control Yujie Tang, Yang Zheng, Na Li

Approximate Distributionally Robust Nonlinear Optimization with Application to Model Predictive Control: A Functional Approach Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu

Approximate Midpoint Policy Iteration for Linear Quadratic Control Benjamin Gravell, Iman Shames, Tyler Summers

ARDL - A Library for Adaptive Robotic Dynamics Learning Joshua Smith, Michael Mistry

Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution Elliott Skomski, Ján Drgoňa, Aaron Tuor

Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty

Bridging Physics-Based and Data-Driven Modeling for Learning Dynamical Systems Rui Wang, Danielle Maddix, Christos Faloutsos, Yuyang Wang, Rose Yu

Cautious Bayesian Optimization for Efficient and Scalable Policy Search Lukas P. Fröhlich, Melanie N. Zeilinger, Edgar D. Klenske

Certainty Equivalent Perception-Based Control Sarah Dean, Benjamin Recht

Certifying Incremental Quadratic Constraints for Neural Networks via Convex Optimization Navid Hashemi, Justin Ruths, Mahyar Fazlyab

Chance-Constrained Quasi-Convex Optimization with Application to Data-Driven Switched Systems Control Guillaume O. Berger, Raphaël M. Jungers, Zheming Wang

Contraction L1-Adaptive Control Using Gaussian Processes Aditya Gahlawat, Arun Lakshmanan, Lin Song, Andrew Patterson, Zhuohuan Wu, Naira Hovakimyan, Evangelos A. Theodorou

Control of Unknown (Linear) Systems with Receding Horizon Learning Christian Ebenbauer, Fabian Pfitz, Shuyou Yu

Data-Driven Abstraction of Monotone Systems Anas Makdesi, Antoine Girard, Laurent Fribourg

Data-Driven Controller Design via Finite-Horizon Dissipativity Nils Wieler, Julian Berberich, Anne Koch, Frank Allgöwer

Data-Driven Design of Switching Reference Governors for Brake-by-Wire Applications Andrea Sassella, Valentina Breschi, Simone Formentin

Data-Driven Reachability Analysis Using Matrix Zonotopes Amr Alanwar, Anne Koch, Frank Allgöwer, Karl Henrik Johansson

Data-Driven System Level Synthesis Anton Xue, Nikolai Matni

Decoupling Dynamics and Sampling: RNNs for Unevenly Sampled Data and Flexible Online Predictions Signe Moe, Camilla Sterud

Domain Adaptation Using System Invariant Dynamics Models Sean J. Wang, Aaron M. Johnson

Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety Noel Csomay-Shanklin, Ryan K. Cosner, Min Dai, Andrew J. Taylor, Aaron D. Ames

Estimating Disentangled Belief About Hidden State and Hidden Task for Meta-Reinforcement Learning Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo

Exploiting Sparsity for Neural Network Verification Matthew Newton, Antonis Papachristodoulou

Fast Stochastic Kalman Gradient Descent for Reinforcement Learning Simone Totaro, Anders Jonsson

Faster Policy Learning with Continuous-Time Gradients Samuel Ainsworth, Kendall Lowrey, John Thickstun, Zaid Harchaoui, Siddhartha Srinivasa

Feedback from Pixels: Output Regulation via Learning-Based Scene View Synthesis Murad Abu-Khalaf, Sertac Karaman, Daniela Rus

Finite-Time System Identification and Adaptive Control in Autoregressive Exogenous Systems Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar

Forced Variational Integrator Networks for Prediction and Control of Mechanical Systems Aaron Havens, Girish Chowdhary

Generating Adversarial Disturbances for Controller Verification Udaya Ghai, David Snyder, Anirudha Majumdar, Elad Hazan

Graph Neural Networks for Distributed Linear-Quadratic Control Fernando Gama, Somayeh Sojoudi

How Are Learned Perception-Based Controllers Impacted by the Limits of Robust Control? Jingxi Xu, Bruce Lee, Nikolai Matni, Dinesh Jayaraman

Improved Analysis for Dynamic Regret of Strongly Convex and Smooth Functions Peng Zhao, Lijun Zhang

Input Convex Neural Networks for Building MPC Felix Bünning, Adrian Schalbetter, Ahmed Aboudonia, Mathias Hudoba de Badyn, Philipp Heer, John Lygeros

Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning Anoopkumar Sonar, Vincent Pacelli, Anirudha Majumdar

KPC: Learning-Based Model Predictive Control with Deterministic Guarantees Emilio T. Maddalena, Paul Scharnhorst, Yuning Jiang, Colin N. Jones

Learning Approximate Forward Reachable Sets Using Separating Kernels Adam J. Thorpe, Kendric R. Ortiz, Meeko M. K. Oishi

Learning Based Attacks in Cyber Physical Systems: Exploration, Detection, and Control Cost Trade-Offs Anshuka Rangi, Mohammad Javad Khojasteh, Massimo Franceschetti

Learning Finite-Dimensional Representations for Koopman Operators Mohammad Khosravi

Learning How to Solve “Bubble Ball” Hotae Lee, Monimoy Bujarbaruah, Francesco Borrelli

Learning Local Modules in Dynamic Networks Paul M.J. Van den Hof, Karthik R. Ramaswamy

Learning Partially Observed Linear Dynamical Systems from Logarithmic Number of Samples Salar Fattahi

Learning Recurrent Neural Net Models of Nonlinear Systems Joshua Hanson, Maxim Raginsky, Eduardo Sontag

Learning Stabilizing Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory Lenart Treven, Sebastian Curi, Mojmír Mutný, Andreas Krause

Learning the Dynamics of Time Delay Systems with Trainable Delays Xunbi A. Ji, Tamás G. Molnár, Sergei S. Avedisov, Gábor Orosz

Learning to Actively Reduce Memory Requirements for Robot Control Tasks Meghan Booker, Anirudha Majumdar

Learning Visually Guided Latent Actions for Assistive Teleoperation Siddharth Karamcheti, Albert J. Zhai, Dylan P. Losey, Dorsa Sadigh

Learning Without Knowing: Unobserved Context in Continuous Transfer Reinforcement Learning Chenyu Liu, Yan Zhang, Yi Shen, Michael M. Zavlanos

Learning-Based Feedforward Augmentation for Steady State Rejection of Residual Dynamics on a Nanometer-Accurate Planar Actuator System Ioannis Proimadis, Yorick Broens, Roland Tóth, Hans Butler

Learning-Based State Reconstruction for a Scalar Hyperbolic PDE Under Noisy Lagrangian Sensing Matthieu Barreau, John Liu, Karl Henrik Johansson

LEOC: A Principled Method in Integrating Reinforcement Learning and Classical Control Theory Naifu Zhang, Nicholas Capel

Linear Regression over Networks with Communication Guarantees Konstantinos Gatsis

Maximum Likelihood Signal Matrix Model for Data-Driven Predictive Control Mingzhou Yin, Andrea Iannelli, Roy S. Smith

Minimax Adaptive Control for a Finite Set of Linear Systems Anders Rantzer

Near-Optimal Data Source Selection for Bayesian Learning Lintao Ye, Aritra Mitra, Shreyas Sundaram

Nested Mixture of Experts: Cooperative and Competitive Learning of Hybrid Dynamical System Junhyeok Ahn, Luis Sentis

Neural Lyapunov Redesign Arash Mehrjou, Mohammad Ghavamzadeh, Bernhard Schölkopf

Non-Conservative Design of Robust Tracking Controllers Based on Input-Output Data Liang Xu, Mustafa Sahin Turan, Baiwei Guo, Giancarlo Ferrari-Trecate

Nonlinear Data-Enabled Prediction and Control Yingzhao Lian, Colin N. Jones

Nonlinear State-Space Identification Using Deep Encoder Networks Gerben Beintema, Roland Toth, Maarten Schoukens

Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance Thinh T. Doan

Offline Reinforcement Learning from Images with Latent Space Models Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn

Offset-Free Setpoint Tracking Using Neural Network Controllers Patricia Pauli, Johannes Köhler, Julian Berberich, Anne Koch, Frank Allgöwer

On Exploration Requirements for Learning Safety Constraints Pierre-François Massiani, Steve Heim, Sebastian Trimpe

On the Model-Based Stochastic Value Gradient for Continuous Reinforcement Learning Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson

On Uninformative Optimal Policies in Adaptive LQR with Unknown B-Matrix Ingvar Ziemann, Henrik Sandberg

Optimal Algorithms for Submodular Maximization with Distributed Constraints Alexander Robey, Arman Adibi, Brent Schlotfeldt, Hamed Hassani, George J. Pappas

Optimal Cost Design for Model Predictive Control Avik Jain, Lawrence Chan, Daniel S. Brown, Anca D. Dragan

Physics-Penalised Regularisation for Learning Dynamics Models with Contact Gabriella Pizzuto, Michael Mistry

Primal-Dual Learning for the Model-Free Risk-Constrained Linear Quadratic Regulator Feiran Zhao, Keyou You

Probabilistic Robust Linear Quadratic Regulators with Gaussian Processes Alexander Rohr, Matthias Neumann-Brosig, Sebastian Trimpe

Provably Sample Efficient Reinforcement Learning in Competitive Linear Quadratic Systems Jingwei Zhang, Zhuoran Yang, Zhengyuan Zhou, Zhaoran Wang

Regret Bounds for Adaptive Nonlinear Control Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine

Regret-Optimal Measurement-Feedback Control Gautam Goel, Babak Hassibi

Reward Biased Maximum Likelihood Estimation for Reinforcement Learning Akshay Mete, Rahul Singh, Xi Liu, P. R. Kumar

Robust Error Bounds for Quantised and Pruned Neural Networks Jiaqi Li, Ross Drummond, Stephen R. Duncan

Robust Reinforcement Learning: A Constrained Game-Theoretic Approach Jing Yu, Clement Gehring, Florian Schäfer, Animashree Anandkumar

Safe Bayesian Optimisation for Controller Design by Utilising the Parameter Space Approach Lorenz Dörschel, David Stenger, Dirk Abel

Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks Liyuan Zheng, Yuanyuan Shi, Lillian J. Ratliff, Baosen Zhang

Safe Reinforcement Learning Using Robust Action Governor Yutong Li, Nan Li, H. Eric Tseng, Anouck Girard, Dimitar Filev, Ilya Kolmanovsky

Safely Learning Dynamical Systems from Short Trajectories Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu

Sample Complexity of Linear Quadratic Gaussian (LQG) Control for Output Feedback Systems Yang Zheng, Luca Furieri, Maryam Kamgarpour, Na Li

SEAGuL: Sample Efficient Adversarially Guided Learning of Value Functions Benoit Landry, Hongkai Dai, Marco Pavone

Self-Supervised Learning of Long-Horizon Manipulation Tasks with Finite-State Task Machines Junchi Liang, Abdeslam Boularias

Sequential Topological Representations for Predictive Models of Deformable Objects Rika Antonova, Anastasia Varava, Peiyang Shi, J. Frederico Carvalho, Danica Kragic

Stability and Identification of Random Asynchronous Linear Time-Invariant Systems Sahin Lale, Oguzhan Teke, Babak Hassibi, Anima Anandkumar

Stable Online Control of Linear Time-Varying Systems Guannan Qu, Yuanyuan Shi, Sahin Lale, Anima Anandkumar, Adam Wierman

Suboptimal Coverings for Continuous Spaces of Control Tasks James A. Preiss, Gaurav S. Sukhatme

The Benefits of Sharing: A Cloud-Aided Performance-Driven Framework to Learn Optimal Feedback Policies Laura Ferrarotti, Valentina Breschi, Alberto Bemporad

The Dynamics of Gradient Descent for Overparametrized Neural Networks Siddhartha Satpathi, R Srikant

The Impact of Data on the Stability of Learning-Based Control Armin Lederer, Alexandre Capone, Thomas Beckers, Jonas Umlauft, Sandra Hirche

Tight Sampling and Discarding Bounds for Scenario Programs with an Arbitrary Number of Removed Samples Licio Romao, Kostas Margellos, Antonis Papachristodoulou

Traffic Forecasting Using Vehicle-to-Vehicle Communication Steven Wong, Lejun Jiang, Robin Walters, Tamás G. Molnár, Gábor Orosz, Rose Yu

Training Deep Residual Networks for Uniform Approximation Guarantees Matteo Marchi, Bahman Gharesifard, Paulo Tabuada

Uncertain-Aware Safe Exploratory Planning Using Gaussian Process and Neural Control Contraction Metric Dawei Sun, Mohammad Javad Khojasteh, Shubhanshu Shekhar, Chuchu Fan

When to Stop Value Iteration: Stability and Near-Optimality Versus Computation Mathieu Granzotto, Romain Postoyan, Dragan Nešić, Lucian Buşoniu, Jamal Daafouz