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ORCID2nd L4DC 2020: Online Event, Berkeley, CA, USA
- Alexandre M. Bayen, Ali Jadbabaie, George J. Pappas, Pablo A. Parrilo, Benjamin Recht, Claire J. Tomlin, Melanie N. Zeilinger:
Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, L4DC 2020, Online Event, Berkeley, CA, USA, 11-12 June 2020. Proceedings of Machine Learning Research 120, PMLR 2020 - Preface. 1-4
- Mona Buisson-Fenet, Friedrich Solowjow, Sebastian Trimpe:
Actively Learning Gaussian Process Dynamics. 5-15 - Yue Sun, Samet Oymak, Maryam Fazel:
Finite Sample System Identification: Optimal Rates and the Role of Regularization. 16-25 - Thinh T. Doan, Justin Romberg:
Finite-Time Performance of Distributed Two-Time-Scale Stochastic Approximation. 26-36 - Valentina Breschi, Simone Formentin:
Virtual Reference Feedback Tuning with data-driven reference model selection. 37-45 - Valentina Breschi, Simone Formentin:
Direct Data-Driven Control with Embedded Anti-Windup Compensation. 46-54 - Trevor D. Ruiz
, Sharmodeep Bhattacharyya, Mahesh Balasubramanian, Kristofer E. Bouchard:
Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models. 55-64 - Abulikemu Abuduweili, Changliu Liu:
Robust Online Model Adaptation by Extended Kalman Filter with Exponential Moving Average and Dynamic Multi-Epoch Strategy. 65-74 - Alex Devonport, Murat Arcak:
Estimating Reachable Sets with Scenario Optimization. 75-84 - Fabio Bonassi, Enrico Terzi, Marcello Farina, Riccardo Scattolini:
LSTM Neural Networks: Input to State Stability and Probabilistic Safety Verification. 85-94 - Ismail Senöz, Albert Podusenko, Wouter M. Kouw, Bert de Vries:
Bayesian joint state and parameter tracking in autoregressive models. 95-104 - Nam Hee Kim, Zhaoming Xie, Michiel van de Panne:
Learning to Correspond Dynamical Systems. 105-117 - Sandeep Menta, Joseph Warrington, John Lygeros, Manfred Morari:
Learning solutions to hybrid control problems using Benders cuts. 118-126 - Xunbi A. Ji, Tamás G. Molnár, Sergei S. Avedisov, Gábor Orosz:
Feed-forward Neural Networks with Trainable Delay. 127-136 - Monimoy Bujarbaruah, Charlott Vallon:
Exploiting Model Sparsity in Adaptive MPC: A Compressed Sensing Viewpoint. 137-146 - Sebastian Curi, Silvan Melchior, Felix Berkenkamp, Andreas Krause:
Structured Variational Inference in Partially Observable UnstableGaussian Process State Space Models. 147-157 - Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis:
Regret Bound for Safe Gaussian Process Bandit Optimization. 158-159 - Jonas Umlauft, Thomas Beckers, Alexandre Capone, Armin Lederer, Sandra Hirche:
Smart Forgetting for Safe Online Learning with Gaussian Processes. 160-169 - Signe Moe, Filippo Remonato, Esten Ingar Grøtli, Jan Tommy Gravdahl:
Linear Antisymmetric Recurrent Neural Networks. 170-178 - Kaiqing Zhang, Bin Hu, Tamer Basar:
Policy Optimization for H2 Linear Control with H∞ Robustness Guarantee: Implicit Regularization and Global Convergence. 179-190 - Rodrigo A. González, Cristian R. Rojas:
A Finite-Sample Deviation Bound for Stable Autoregressive Processes. 191-200 - Xuezhou Zhang, Xiaojin Zhu, Laurent Lessard:
Online Data Poisoning Attacks. 201-210 - Napat Karnchanachari, Miguel de la Iglesia Valls, David Hoeller, Marco Hutter:
Practical Reinforcement Learning For MPC: Learning from sparse objectives in under an hour on a real robot. 211-224 - Andreas Rene Geist, Sebastian Trimpe:
Learning Constrained Dynamics with Gauss' Principle adhering Gaussian Processes. 225-234 - Luiz F. O. Chamon, Santiago Paternain, Alejandro Ribeiro:
Counterfactual Programming for Optimal Control. 235-244 - Tianyu Wang, Vikas Dhiman, Nikolay Atanasov:
Learning Navigation Costs from Demonstrations with Semantic Observations. 245-255 - Guannan Qu, Adam Wierman, Na Li:
Scalable Reinforcement Learning of Localized Policies for Multi-Agent Networked Systems. 256-266 - Mirko Mazzoleni, Matteo Scandella, Simone Formentin, Fabio Previdi:
Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach. 267-276 - Homanga Bharadhwaj, Kevin Xie, Florian Shkurti:
Model-Predictive Control via Cross-Entropy and Gradient-Based Optimization. 277-286 - Luca Furieri, Yang Zheng, Maryam Kamgarpour:
Learning the Globally Optimal Distributed LQ Regulator. 287-297 - Reza Khodayi-Mehr, Michael M. Zavlanos:
VarNet: Variational Neural Networks for the Solution of Partial Differential Equations. 298-307 - Harish K. Venkataraman, Derya Aksaray, Peter Seiler:
Tractable Reinforcement Learning of Signal Temporal Logic Objectives. 308-317 - Jasmine Sekhon, Cody H. Fleming:
A Spatially and Temporally Attentive Joint Trajectory Prediction Framework for Modeling Vessel Intent. 318-327 - Jayesh K. Gupta, Kunal Menda, Zachary Manchester, Mykel J. Kochenderfer:
Structured Mechanical Models for Robot Learning and Control. 328-337 - Sivaranjani S., Etika Agarwal, Vijay Gupta:
Data-driven Identification of Approximate Passive Linear Models for Nonlinear Systems. 338-339 - Aidan Laracy, Hamid R. Ossareh:
Constraint Management for Batch Processes Using Iterative Learning Control and Reference Governors. 340-349 - Sarah Dean, Nikolai Matni, Benjamin Recht, Vickie Ye:
Robust Guarantees for Perception-Based Control. 350-360 - Akshay Agrawal, Shane T. Barratt, Stephen P. Boyd, Bartolomeo Stellato:
Learning Convex Optimization Control Policies. 361-373 - Malayandi Palan, Shane T. Barratt, Alex McCauley, Dorsa Sadigh, Vikas Sindhwani, Stephen P. Boyd:
Fitting a Linear Control Policy to Demonstrations with a Kalman Constraint. 374-383 - Joshua Hanson, Maxim Raginsky:
Universal Simulation of Stable Dynamical Systems by Recurrent Neural Nets. 384-392 - Max Revay, Ian R. Manchester:
Contracting Implicit Recurrent Neural Networks: Stable Models with Improved Trainability. 393-403 - Anguluri Rajasekhar, Abed AlRahman Al Makdah, Vaibhav Katewa, Fabio Pasqualetti:
On the Robustness of Data-Driven Controllers for Linear Systems. 404-412 - Joan Bas-Serrano, Gergely Neu:
Faster saddle-point optimization for solving large-scale Markov decision processes. 413-423 - Lukas Hewing, Elena Arcari, Lukas P. Fröhlich, Melanie N. Zeilinger:
On Simulation and Trajectory Prediction with Gaussian Process Dynamics. 424-434 - Anastasios Tsiamis, Nikolai Matni, George J. Pappas:
Sample Complexity of Kalman Filtering for Unknown Systems. 435-444 - Achin Jain, Francesco Smarra, Enrico Reticcioli, Alessandro D'Innocenzo, Manfred Morari:
NeurOpt: Neural network based optimization for building energy management and climate control. 445-454 - Kim Peter Wabersich, Melanie N. Zeilinger:
Bayesian model predictive control: Efficient model exploration and regret bounds using posterior sampling. 455-464 - Armin Lederer, Alexandre Capone, Sandra Hirche:
Parameter Optimization for Learning-based Control of Control-Affine Systems. 465-475 - Mohammad Akbari, Bahman Gharesifard, Tamás Linder:
Riccati updates for online linear quadratic control. 476-485 - Jianqing Fan, Zhaoran Wang, Yuchen Xie, Zhuoran Yang:
A Theoretical Analysis of Deep Q-Learning. 486-489 - Alexandre Capone, Gerrit Noske, Jonas Umlauft, Thomas Beckers, Armin Lederer, Sandra Hirche:
Localized active learning of Gaussian process state space models. 490-499 - Anjian Li, Somil Bansal, Georgios Giovanis, Varun Tolani, Claire J. Tomlin, Mo Chen:
Generating Robust Supervision for Learning-Based Visual Navigation Using Hamilton-Jacobi Reachability. 500-510 - Janine Matschek, Rolf Findeisen:
Learning supported Model Predictive Control for Tracking of Periodic References. 511-520 - Peter Coppens, Mathijs Schuurmans, Panagiotis Patrinos:
Data-driven distributionally robust LQR with multiplicative noise. 521-530 - Hesameddin Mohammadi, Mihailo R. Jovanovic, Mahdi Soltanolkotabi:
Learning the model-free linear quadratic regulator via random search. 531-539 - Yuchao Li, Karl Henrik Johansson, Jonas Mårtensson:
Lambda-Policy Iteration with Randomization for Contractive Models with Infinite Policies: Well-Posedness and Convergence. 540-549 - Jack Umenberger, Thomas B. Schön:
Optimistic robust linear quadratic dual control. 550-560 - Manxi Wu, Saurabh Amin, Asuman E. Ozdaglar:
Bayesian Learning with Adaptive Load Allocation Strategies. 561-570 - Angelo Domenico Bonzanini, Ali Mesbah:
Learning-based Stochastic Model Predictive Control with State-Dependent Uncertainty. 571-580 - Devavrat Shah, Qiaomin Xie, Zhi Xu:
Stable Reinforcement Learning with Unbounded State Space. 581 - Donghwan Lee, Niao He:
Periodic Q-Learning. 582-598 - Benjamin Gravell, Tyler H. Summers:
Robust Learning-Based Control via Bootstrapped Multiplicative Noise. 599-607 - Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, Yisong Yue:
Robust Regression for Safe Exploration in Control. 608-619 - Liyuan Zheng, Lillian J. Ratliff:
Constrained Upper Confidence Reinforcement Learning. 620-629 - Muhammad Asif Rana, Anqi Li, Dieter Fox, Byron Boots, Fabio Ramos, Nathan D. Ratliff:
Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems. 630-639 - Nathanael Bosch, Jan Achterhold, Laura Leal-Taixé, Jörg Stückler:
Planning from Images with Deep Latent Gaussian Process Dynamics. 640-650 - Kun Wang, Mridul Aanjaneya, Kostas E. Bekris:
A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via Differentiable Physics Engines. 651-665 - Zeyu Jia, Lin Yang, Csaba Szepesvári, Mengdi Wang:
Model-Based Reinforcement Learning with Value-Targeted Regression. 666-686 - Soojean Han:
Localized Learning of Robust Controllers for Networked Systems with Dynamic Topology. 687-696 - Manish Goyal, Parasara Sridhar Duggirala:
NeuralExplorer: State Space Exploration of Closed Loop Control Systems Using Neural Networks. 697 - Mark Boyer:
Toward fusion plasma scenario planning for NSTX-U using machine-learning-accelerated models. 698-707 - Andrew J. Taylor, Andrew Singletary, Yisong Yue, Aaron D. Ames:
Learning for Safety-Critical Control with Control Barrier Functions. 708-717 - Amir Ali Ahmadi, Bachir El Khadir:
Learning Dynamical Systems with Side Information. 718-727 - Marcus Pereira, Ziyi Wang, Tianrong Chen, Emily A. Reed, Evangelos A. Theodorou:
Feynman-Kac Neural Network Architectures for Stochastic Control Using Second-Order FBSDE Theory. 728-738 - Jeongho Kim, Insoon Yang:
Hamilton-Jacobi-Bellman Equations for Q-Learning in Continuous Time. 739-748 - Changkyu Song, Abdeslam Boularias:
Identifying Mechanical Models of Unknown Objects with Differentiable Physics Simulations. 749-760 - Nathan O. Lambert, Brandon Amos, Omry Yadan, Roberto Calandra:
Objective Mismatch in Model-based Reinforcement Learning. 761-770 - Avishai Sintov, Andrew Kimmel, Bowen Wen, Abdeslam Boularias, Kostas E. Bekris:
Tools for Data-driven Modeling of Within-Hand Manipulation with Underactuated Adaptive Hands. 771-780 - Mohammad Javad Khojasteh, Vikas Dhiman, Massimo Franceschetti, Nikolay Atanasov:
Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics. 781-792 - Colin Summers, Kendall Lowrey, Aravind Rajeswaran, Siddhartha S. Srinivasa, Emanuel Todorov:
Lyceum: An efficient and scalable ecosystem for robot learning. 793-803 - Giovanni Sutanto, Austin S. Wang, Yixin Lin, Mustafa Mukadam, Gaurav S. Sukhatme, Akshara Rai, Franziska Meier:
Encoding Physical Constraints in Differentiable Newton-Euler Algorithm. 804-813 - Yingying Li, Yujie Tang, Runyu Zhang, Na Li:
Distributed Reinforcement Learning for Decentralized Linear Quadratic Control: A Derivative-Free Policy Optimization Approach. 814 - Tim Seyde, Wilko Schwarting, Sertac Karaman, Daniela Rus:
Learning to Plan via Deep Optimistic Value Exploration. 815-825 - Aditya Gahlawat, Pan Zhao, Andrew Patterson, Naira Hovakimyan, Evangelos A. Theodorou:
L1-GP: L1 Adaptive Control with Bayesian Learning. 826-837 - Ahmed Allibhoy, Jorge Cortés:
Data-Driven Distributed Predictive Control via Network Optimization. 838-839 - Mohak Bhardwaj, Ankur Handa, Dieter Fox, Byron Boots:
Information Theoretic Model Predictive Q-Learning. 840-850 - Dylan J. Foster, Tuhin Sarkar, Alexander Rakhlin:
Learning nonlinear dynamical systems from a single trajectory. 851-861 - Hao Gong, Mengdi Wang:
A Duality Approach for Regret Minimization in Average-Award Ergodic Markov Decision Processes. 862-883 - Jacob H. Seidman, Mahyar Fazlyab, Victor M. Preciado, George J. Pappas:
Robust Deep Learning as Optimal Control: Insights and Convergence Guarantees. 884-893 - Elena Arcari, Lukas Hewing, Max Schlichting, Melanie N. Zeilinger:
Dual Stochastic MPC for Systems with Parametric and Structural Uncertainty. 894-903 - Hany Abdulsamad, Jan Peters:
Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation. 904-914 - Jia-Jie Zhu, Bernhard Schölkopf, Moritz Diehl:
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control. 915-923 - Amrit Singh Bedi, Dheeraj Peddireddy, Vaneet Aggarwal, Alec Koppel:
Efficient Large-Scale Gaussian Process Bandits by Believing only Informative Actions. 924-934 - Ge Yang, Amy Zhang, Ari S. Morcos, Joelle Pineau, Pieter Abbeel, Roberto Calandra:
Plan2Vec: Unsupervised Representation Learning by Latent Plans. 935-946 - Joao Paulo Jansch-Porto, Bin Hu, Geir E. Dullerud:
Policy Learning of MDPs with Mixed Continuous/Discrete Variables: A Case Study on Model-Free Control of Markovian Jump Systems. 947-957 - Rahul Singh, Qinsheng Zhang, Yongxin Chen:
Improving Robustness via Risk Averse Distributional Reinforcement Learning. 958-968 - Karl Pertsch, Oleh Rybkin, Jingyun Yang, Shenghao Zhou, Konstantinos G. Derpanis, Kostas Daniilidis, Joseph J. Lim, Andrew Jaegle:
Keyframing the Future: Keyframe Discovery for Visual Prediction and Planning. 969-979 - Ilnura Usmanova, Andreas Krause, Maryam Kamgarpour:
Safe non-smooth black-box optimization with application to policy search. 980-989 - Fernando Castañeda, Mathias Wulfman, Ayush Agrawal, Tyler Westenbroek, Shankar Sastry, Claire J. Tomlin, Koushil Sreenath:
Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning. 990-999 - Alessandro Falsone, Federico Molinari, Maria Prandini:
Uncertain multi-agent MILPs: A data-driven decentralized solution with probabilistic feasibility guarantees. 1000-1009
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