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3rd L4DC 2021: Virtual Event, Switzerland
- Ali Jadbabaie, John Lygeros, George J. Pappas, Pablo A. Parrilo, Benjamin Recht, Claire J. Tomlin, Melanie N. Zeilinger:
Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, L4DC 2021, 7-8 June 2021, Virtual Event, Switzerland. Proceedings of Machine Learning Research 144, PMLR 2021 - Ali Jadbabaie, John Lygeros, George J. Pappas, Pablo A. Parrilo, Benjamin Recht, Claire J. Tomlin, Melanie N. Zeilinger:
Preface. 1-5 - Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson:
On the model-based stochastic value gradient for continuous reinforcement learning. 6-20 - Anoopkumar Sonar, Vincent Pacelli, Anirudha Majumdar:
Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning. 21-33 - Matthieu Barreau, John Liu, Karl Henrik Johansson:
Learning-based State Reconstruction for a Scalar Hyperbolic PDE under noisy Lagrangian Sensing. 34-46 - Thinh T. Doan:
Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance. 47 - Peng Zhao, Lijun Zhang:
Improved Analysis for Dynamic Regret of Strongly Convex and Smooth Functions. 48-59 - Salar Fattahi:
Learning Partially Observed Linear Dynamical Systems from Logarithmic Number of Samples. 60-72 - Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo:
Estimating Disentangled Belief about Hidden State and Hidden Task for Meta-Reinforcement Learning. 73-86 - Laura Ferrarotti, Valentina Breschi, Alberto Bemporad:
The benefits of sharing: a cloud-aided performance-driven framework to learn optimal feedback policies. 87-98 - Andrea Sassella
, Valentina Breschi, Simone Formentin:
Data-driven design of switching reference governors for brake-by-wire applications. 99-110 - Fernando Gama, Somayeh Sojoudi:
Graph Neural Networks for Distributed Linear-Quadratic Control. 111-124 - Meghan Booker, Anirudha Majumdar:
Learning to Actively Reduce Memory Requirements for Robot Control Tasks. 125-137 - Liang Xu, Mustafa Sahin Turan, Baiwei Guo, Giancarlo Ferrari-Trecate:
Non-conservative Design of Robust Tracking Controllers Based on Input-output Data. 138-149 - Alexander Robey, Arman Adibi, Brent Schlotfeldt, Hamed Hassani, George J. Pappas:
Optimal Algorithms for Submodular Maximization with Distributed Constraints. 150-162 - Amr Alanwar, Anne Koch, Frank Allgöwer, Karl Henrik Johansson:
Data-Driven Reachability Analysis Using Matrix Zonotopes. 163-175 - Paul M. J. Van den Hof, Karthik Raghavan Ramaswamy:
Learning local modules in dynamic networks. 176-188 - Anton Xue, Nikolai Matni:
Data-Driven System Level Synthesis. 189-200 - Adam J. Thorpe, Kendric R. Ortiz, Meeko M. K. Oishi:
Learning Approximate Forward Reachable Sets Using Separating Kernels. 201-212 - Ingvar M. Ziemann, Henrik Sandberg:
On Uninformative Optimal Policies in Adaptive LQR with Unknown B-Matrix. 213-226 - Lukas P. Fröhlich, Melanie N. Zeilinger, Edgar D. Klenske:
Cautious Bayesian Optimization for Efficient and Scalable Policy Search. 227-240 - Gerben Beintema, Roland Tóth, Maarten Schoukens:
Nonlinear state-space identification using deep encoder networks. 241-250 - Felix Bünning, Adrian Schalbetter, Ahmed Aboudonia, Mathias Hudoba de Badyn, Philipp Heer, John Lygeros:
Input Convex Neural Networks for Building MPC. 251-262 - Benoît Legat, Raphaël M. Jungers, Jean Bouchat:
Abstraction-based branch and bound approach to Q-learning for hybrid optimal control. 263-274 - Clara Lucía Galimberti, Liang Xu, Giancarlo Ferrari-Trecate:
A unified framework for Hamiltonian deep neural networks. 275-286 - Nils Wieler, Julian Berberich, Anne Koch, Frank Allgöwer:
Data-Driven Controller Design via Finite-Horizon Dissipativity. 287-298 - Lorenz Dörschel, David Stenger, Dirk Abel:
Safe Bayesian Optimisation for Controller Design by Utilising the Parameter Space Approach. 299-311 - Licio Romao, Kostas Margellos, Antonis Papachristodoulou:
Tight sampling and discarding bounds for scenario programs with an arbitrary number of removed samples. 312-323 - Alexander von Rohr, Matthias Neumann-Brosig
Probabilistic robust linear quadratic regulators with Gaussian processes. 324-335 - Liyuan Zheng, Yuanyuan Shi, Lillian J. Ratliff, Baosen Zhang:
Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks. 336-347 - Rika Antonova, Anastasiia Varava, Peiyang Shi, J. Frederico Carvalho, Danica Kragic:
Sequential Topological Representations for Predictive Models of Deformable Objects. 348-360 - Jiaqi Li, Ross Drummond, Stephen R. Duncan:
Robust error bounds for quantised and pruned neural networks. 361-372 - Siddhartha Satpathi, Rayadurgam Srikant:
The Dynamics of Gradient Descent for Overparametrized Neural Networks. 373-384 - Rui Wang, Danielle C. Maddix, Christos Faloutsos, Yuyang Wang, Rose Yu:
Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems. 385-398 - Sarah Dean, Benjamin Recht:
Certainty Equivalent Perception-Based Control. 399-411 - Mathieu Granzotto, Romain Postoyan, Dragan Nesic, Lucian Busoniu, Jamal Daafouz:
When to stop value iteration: stability and near-optimality versus computation. 412-424 - Joshua Hanson, Maxim Raginsky, Eduardo D. Sontag:
Learning Recurrent Neural Net Models of Nonlinear Systems. 425-435 - Mario Sznaier:
A Data Driven, Convex Optimization Approach to Learning Koopman Operators. 436-446 - Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra:
Accelerating Distributed SGD for Linear Regression using Iterative Pre-Conditioning. 447-458 - Arash Mehrjou, Mohammad Ghavamzadeh, Bernhard Schölkopf:
Neural Lyapunov Redesign. 459-470 - Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine:
Regret Bounds for Adaptive Nonlinear Control. 471-483 - Junchi Liang, Abdeslam Boularias:
Self-Supervised Learning of Long-Horizon Manipulation Tasks with Finite-State Task Machines. 484-497 - Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu:
Safely Learning Dynamical Systems from Short Trajectories. 498-509 - Ziyi Wang, Oswin So, Keuntaek Lee, Evangelos A. Theodorou:
Adaptive Risk Sensitive Model Predictive Control with Stochastic Search. 510-522 - Ying Zhao Lian, Colin N. Jones:
Nonlinear Data-Enabled Prediction and Control. 523-534 - Ioannis Proimadis, Yorick Broens, Roland Tóth, Hans Butler:
Learning-based feedforward augmentation for steady state rejection of residual dynamics on a nanometer-accurate planar actuator system. 535-546 - James A. Preiss, Gaurav S. Sukhatme:
Suboptimal coverings for continuous spaces of control tasks. 547-558 - Yang Zheng, Luca Furieri, Maryam Kamgarpour, Na Li:
Sample Complexity of Linear Quadratic Gaussian (LQG) Control for Output Feedback Systems. 559-570 - Guillaume O. Berger, Raphaël M. Jungers, Zheming Wang:
Chance-constrained quasi-convex optimization with application to data-driven switched systems control. 571-583 - Christian Ebenbauer, Fabian Pfitz, Shuyou Yu:
Control of Unknown (Linear) Systems with Receding Horizon Learning. 584-596 - Jingwei Zhang, Zhuoran Yang, Zhengyuan Zhou, Zhaoran Wang:
Provably Sample Efficient Reinforcement Learning in Competitive Linear Quadratic Systems. 597-598 - Yujie Tang, Yang Zheng, Na Li:
Analysis of the Optimization Landscape of Linear Quadratic Gaussian (LQG) Control. 599-610 - Gabriella Pizzuto, Michael N. Mistry:
Physics-penalised Regularisation for Learning Dynamics Models with Contact. 611-622 - Armin Lederer, Alexandre Capone, Thomas Beckers, Jonas Umlauft, Sandra Hirche:
The Impact of Data on the Stability of Learning-Based Control. 623-635 - Joseph E. Gaudio, Anuradha M. Annaswamy, José M. Moreu, Michael A. Bolender, Travis E. Gibson:
Accelerated Learning with Robustness to Adversarial Regressors. 636-650 - Sahin Lale, Oguzhan Teke, Babak Hassibi, Anima Anandkumar:
Stability and Identification of Random Asynchronous Linear Time-Invariant Systems. 651-663 - Lenart Treven, Sebastian Curi, Mojmír Mutný, Andreas Krause:
Learning Stabilizing Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory. 664-676 - Matteo Marchi, Bahman Gharesifard, Paulo Tabuada:
Training deep residual networks for uniform approximation guarantees. 677-688 - Naifu Zhang, Nicholas Capel:
LEOC: A Principled Method in Integrating Reinforcement Learning and Classical Control Theory. 689-701 - Feiran Zhao, Keyou You:
Primal-dual Learning for the Model-free Risk-constrained Linear Quadratic Regulator. 702-714 - Matthew Newton, Antonis Papachristodoulou:
Exploiting Sparsity for Neural Network Verification. 715-727 - Dawei Sun, Mohammad Javad Khojasteh, Shubhanshu Shekhar, Chuchu Fan:
Uncertain-aware Safe Exploratory Planning using Gaussian Process and Neural Control Contraction Metric. 728-741 - Guannan Qu, Yuanyuan Shi, Sahin Lale, Anima Anandkumar, Adam Wierman:
Stable Online Control of Linear Time-Varying Systems. 742-753 - Joshua Smith, Michael N. Mistry:
ARDL - A Library for Adaptive Robotic Dynamics Learning. 754-766 - Konstantinos Gatsis:
Linear Regression over Networks with Communication Guarantees. 767-778 - Junhyeok Ahn, Luis Sentis:
Nested Mixture of Experts: Cooperative and Competitive Learning of Hybrid Dynamical System. 779-790 - Chenyu Liu, Yan Zhang, Yi Shen, Michael M. Zavlanos:
Learning without Knowing: Unobserved Context in Continuous Transfer Reinforcement Learning. 791-802 - Anas Makdesi, Antoine Girard, Laurent Fribourg:
Data-Driven Abstraction of Monotone Systems. 803-814 - Akshay Mete, Rahul Singh, Xi Liu, P. R. Kumar:
Reward Biased Maximum Likelihood Estimation for Reinforcement Learning. 815-827 - Murad Abu-Khalaf, Sertac Karaman, Daniela Rus:
Feedback from Pixels: Output Regulation via Learning-based Scene View Synthesis. 828-841 - Navid Hashemi, Justin Ruths, Mahyar Fazlyab:
Certifying Incremental Quadratic Constraints for Neural Networks via Convex Optimization. 842-853 - Lintao Ye, Aritra Mitra, Shreyas Sundaram:
Near-Optimal Data Source Selection for Bayesian Learning. 854-865 - Daniel Esteban Ochoa, Jorge I. Poveda, Anantharam Subbaraman, Gerd S. Schmidt, Farshad R. Pour Safaei:
Accelerated Concurrent Learning Algorithms via Data-Driven Hybrid Dynamics and Nonsmooth ODEs. 866-878 - Anshuka Rangi, Mohammad Javad Khojasteh, Massimo Franceschetti:
Learning based attacks in Cyber Physical Systems: Exploration, Detection, and Control Cost trade-offs. 879-892 - Anders Rantzer:
Minimax Adaptive Control for a Finite Set of Linear Systems. 893-904 - Pierre-François Massiani, Steve Heim, Sebastian Trimpe:
On exploration requirements for learning safety constraints. 905-916 - Steven Wong, Lejun Jiang, Robin Walters, Tamás G. Molnár, Gábor Orosz, Rose Yu:
Traffic Forecasting using Vehicle-to-Vehicle Communication. 917-929 - Xunbi A. Ji, Tamás G. Molnár, Sergei S. Avedisov, Gábor Orosz:
Learning the Dynamics of Time Delay Systems with Trainable Delays. 930-942 - Signe Moe, Camilla Sterud:
Decoupling dynamics and sampling: RNNs for unevenly sampled data and flexible online predictions. 943-953 - Jingxi Xu, Bruce D. Lee, Nikolai Matni, Dinesh Jayaraman:
How Are Learned Perception-Based Controllers Impacted by the Limits of Robust Control? 954-966 - Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar:
Finite-time System Identification and Adaptive Control in Autoregressive Exogenous Systems. 967-979 - Elliott Skomski, Ján Drgona, Aaron Tuor:
Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution. 980-991 - Patricia Pauli, Johannes Köhler, Julian Berberich, Anne Koch, Frank Allgöwer:
Offset-free setpoint tracking using neural network controllers. 992-1003 - Mingzhou Yin, Andrea Iannelli, Roy S. Smith:
Maximum Likelihood Signal Matrix Model for Data-Driven Predictive Control. 1004-1014 - Emilio Tanowe Maddalena, Paul Scharnhorst, Yuning Jiang, Colin N. Jones:
KPC: Learning-Based Model Predictive Control with Deterministic Guarantees. 1015-1026 - Aditya Gahlawat, Arun Lakshmanan, Lin Song, Andrew Patterson, Zhuohuan Wu, Naira Hovakimyan, Evangelos A. Theodorou:
Contraction ℒ1-Adaptive Control using Gaussian Processes. 1027-1040 - Noel Csomay-Shanklin, Ryan K. Cosner, Min Dai, Andrew J. Taylor, Aaron D. Ames:
Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety. 1041-1053 - Samuel K. Ainsworth, Kendall Lowrey, John Thickstun, Zaïd Harchaoui, Siddhartha S. Srinivasa:
Faster Policy Learning with Continuous-Time Gradients. 1054-1067 - Hotae Lee, Monimoy Bujarbaruah, Francesco Borrelli:
Learning How to Solve "Bubble Ball". 1068-1079 - Benjamin Gravell, Iman Shames, Tyler H. Summers:
Approximate Midpoint Policy Iteration for Linear Quadratic Control. 1080-1092 - Yutong Li, Nan Li, H. Eric Tseng, Anouck Girard, Dimitar P. Filev, Ilya V. Kolmanovsky:
Safe Reinforcement Learning Using Robust Action Governor. 1093-1104 - Benoit Landry, Hongkai Dai, Marco Pavone:
SEAGuL: Sample Efficient Adversarially Guided Learning of Value Functions. 1105-1117 - Simone Totaro, Anders Jonsson:
Fast Stochastic Kalman Gradient Descent for Reinforcement Learning. 1118-1129 - Sean J. Wang, Aaron M. Johnson:
Domain Adaptation Using System Invariant Dynamics Models. 1130-1141 - Aaron J. Havens, Girish Chowdhary:
Forced Variational Integrator Networks for Prediction and Control of Mechanical Systems. 1142-1153 - Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn:
Offline Reinforcement Learning from Images with Latent Space Models. 1154-1168 - Dhruva Kartik, Neeraj Sood, Urbashi Mitra, Tara Javidi
Adaptive Sampling for Estimating Distributions: A Bayesian Upper Confidence Bound Approach. 1169-1179 - Nicholas Galioto, Alex Arkady Gorodetsky:
A New Objective for Identification of Partially Observed Linear Time-Invariant Dynamical Systems from Input-Output Data. 1180-1191 - Udaya Ghai, David Snyder, Anirudha Majumdar, Elad Hazan:
Generating Adversarial Disturbances for Controller Verification. 1192-1204 - Avik Jain, Lawrence Chan, Daniel S. Brown, Anca D. Dragan:
Optimal Cost Design for Model Predictive Control. 1205-1217 - Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty:
Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data. 1218-1229 - Siddharth Karamcheti, Albert J. Zhai, Dylan P. Losey, Dorsa Sadigh:
Learning Visually Guided Latent Actions for Assistive Teleoperation. 1230-1241 - Jing Yu, Clement Gehring, Florian Schäfer
Robust Reinforcement Learning: A Constrained Game-theoretic Approach. 1242-1254 - Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu:
Approximate Distributionally Robust Nonlinear Optimization with Application to Model Predictive Control: A Functional Approach. 1255-1269 - Gautam Goel, Babak Hassibi:
Regret-optimal measurement-feedback control. 1270-1280 - Mohammad Khosravi:
Learning Finite-Dimensional Representations For Koopman Operators. 1281
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