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5th L4DC 2023: Philadelphia, PA, USA
- Nikolai Matni, Manfred Morari, George J. Pappas:
Learning for Dynamics and Control Conference, L4DC 2023, 15-16 June 2023, Philadelphia, PA, USA. Proceedings of Machine Learning Research 211, PMLR 2023 - Karthik Elamvazhuthi, Xuechen Zhang, Samet Oymak, Fabio Pasqualetti:
Learning on Manifolds: Universal Approximations Properties using Geometric Controllability Conditions for Neural ODEs. 1-11 - Saber Jafarpour
, Akash Harapanahalli, Samuel Coogan:
Interval Reachability of Nonlinear Dynamical Systems with Neural Network Controllers. 12-25 - Adithya Ramesh, Balaraman Ravindran:
Physics-Informed Model-Based Reinforcement Learning. 26-37 - Bilgehan Sel, Ahmad Al-Tawaha, Yuhao Ding, Ruoxi Jia, Bo Ji, Javad Lavaei, Ming Jin:
Learning-to-Learn to Guide Random Search: Derivative-Free Meta Blackbox Optimization on Manifold. 38-50 - Yi Tian, Kaiqing Zhang, Russ Tedrake, Suvrit Sra:
Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control? 51-63 - Pengzhi Yang
Policy Learning for Active Target Tracking over Continuous SE(3) Trajectories. 64-75 - Kaustubh Sridhar, Souradeep Dutta, James Weimer, Insup Lee:
Guaranteed Conformance of Neurosymbolic Models to Natural Constraints. 76-89 - Kai-Chieh Hsu, Duy Phuong Nguyen, Jaime Fernández Fisac:
ISAACS: Iterative Soft Adversarial Actor-Critic for Safety. 90-103 - Yikun Cheng, Pan Zhao, Naira Hovakimyan:
Safe and Efficient Reinforcement Learning using Disturbance-Observer-Based Control Barrier Functions. 104-115 - Xunbi A. Ji, Gábor Orosz:
Learning the dynamics of autonomous nonlinear delay systems. 116-127 - Yaofeng Desmond Zhong, Jiequn Han, Biswadip Dey, Georgia Olympia Brikis:
Improving Gradient Computation for Differentiable Physics Simulation with Contacts. 128-141 - Orhan Eren Akgün, Arif Kerem Dayi, Stephanie Gil, Angelia Nedich:
Learning Trust Over Directed Graphs in Multiagent Systems. 142-154 - Kyle Beltran Hatch, Benjamin Eysenbach, Rafael Rafailov, Tianhe Yu, Ruslan Salakhutdinov, Sergey Levine, Chelsea Finn:
Contrastive Example-Based Control. 155-169 - Sheng Cheng, Lin Song, Minkyung Kim, Shenlong Wang, Naira Hovakimyan:
DiffTune+: Hyperparameter-Free Auto-Tuning using Auto-Differentiation. 170-183 - Sarper Aydin, Ceyhun Eksin:
Policy Gradient Play with Networked Agents in Markov Potential Games. 184-195 - Serban Sabau, Yifei Zhang, Sourav Kumar Ukil:
Sample Complexity Bound for Evaluating the Robust Observer's Performance under Coprime Factors Uncertainty. 196-207 - Keyan Miao, Konstantinos Gatsis:
Learning Robust State Observers using Neural ODEs. 208-219 - Rajiv Sambharya, Georgina Hall, Brandon Amos, Bartolomeo Stellato:
End-to-End Learning to Warm-Start for Real-Time Quadratic Optimization. 220-234 - Tejas Pagare, Vivek S. Borkar, Konstantin Avrachenkov:
Full Gradient Deep Reinforcement Learning for Average-Reward Criterion. 235-247 - Yitian Chen
Regret Analysis of Online LQR Control via Trajectory Prediction and Tracking. 248-258 - Yecheng Jason Ma, Kausik Sivakumar, Jason Yan, Osbert Bastani, Dinesh Jayaraman:
Learning Policy-Aware Models for Model-Based Reinforcement Learning via Transition Occupancy Matching. 259-271 - Songyuan Zhang, Yumeng Xiu, Guannan Qu, Chuchu Fan:
Compositional Neural Certificates for Networked Dynamical Systems. 272-285 - Fernando Castañeda, Haruki Nishimura, Rowan Thomas McAllister, Koushil Sreenath, Adrien Gaidon:
In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States. 286-299 - Anushri Dixit
Adaptive Conformal Prediction for Motion Planning among Dynamic Agents. 300-314 - Dongsheng Ding, Xiaohan Wei, Zhuoran Yang, Zhaoran Wang, Mihailo R. Jovanovic:
Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning. 315-332 - Yan Jiang, Wenqi Cui, Baosen Zhang, Jorge Cortés:
Equilibria of Fully Decentralized Learning in Networked Systems. 333-345 - Luke Bhan, Yuanyuan Shi, Miroslav Krstic:
Operator Learning for Nonlinear Adaptive Control. 346-357 - Zhuoyuan Wang, Yorie Nakahira:
A Generalizable Physics-informed Learning Framework for Risk Probability Estimation. 358-370 - Wenqi Cui, Linbin Huang, Weiwei Yang, Baosen Zhang:
Efficient Reinforcement Learning Through Trajectory Generation. 371-382 - Muhammad Abdullah Naeem:
Concentration Phenomenon for Random Dynamical Systems: An Operator Theoretic Approach. 383-394 - Yashaswini Murthy, Mehrdad Moharrami, R. Srikant:
Modified Policy Iteration for Exponential Cost Risk Sensitive MDPs. 395-406 - Taha Entesari, Mahyar Fazlyab:
Automated Reachability Analysis of Neural Network-Controlled Systems via Adaptive Polytopes. 407-419 - Lauren E. Conger, Sydney Vernon, Eric Mazumdar:
Designing System Level Synthesis Controllers for Nonlinear Systems with Stability Guarantees. 420-430 - Kaiyuan Tan, Jun Wang, Yiannis Kantaros:
Targeted Adversarial Attacks against Neural Network Trajectory Predictors. 431-444 - Xiaobing Dai, Armin Lederer, Zewen Yang
Can Learning Deteriorate Control? Analyzing Computational Delays in Gaussian Process-Based Event-Triggered Online Learning. 445-457 - Paul Griffioen
Probabilistic Invariance for Gaussian Process State Space Models. 458-468 - Sampada Deglurkar, Michael H. Lim, Johnathan Tucker, Zachary N. Sunberg, Aleksandra Faust, Claire J. Tomlin:
Compositional Learning-based Planning for Vision POMDPs. 469-482 - Tianqi Cui, Thomas Bertalan, George J. Pappas, Manfred Morari, Yannis G. Kevrekidis, Mahyar Fazlyab:
Certified Invertibility in Neural Networks via Mixed-Integer Programming. 483-496 - Spencer Hutchinson, Berkay Turan, Mahnoosh Alizadeh:
The Impact of the Geometric Properties of the Constraint Set in Safe Optimization with Bandit Feedback. 497-508 - Guillaume O. Berger, Sriram Sankaranarayanan:
Template-Based Piecewise Affine Regression. 509-520 - Thomas Beckers, Qirui Wu, George J. Pappas:
Physics-enhanced Gaussian Process Variational Autoencoder. 521-533 - Leilei Cui, Tamer Basar, Zhong-Ping Jiang:
A Reinforcement Learning Look at Risk-Sensitive Linear Quadratic Gaussian Control. 534-546 - Erfan Aasi, Mingyu Cai, Cristian Ioan Vasile, Calin Belta:
Time-Incremental Learning of Temporal Logic Classifiers Using Decision Trees. 547-559 - Paula Gradu, Elad Hazan, Edgar Minasyan:
Adaptive Regret for Control of Time-Varying Dynamics. 560-572 - Zihao Zhou, Rose Yu:
Automatic Integration for Fast and Interpretable Neural Point Processes. 573-585 - Thomas T. C. K. Zhang, Katie Kang, Bruce D. Lee, Claire J. Tomlin, Sergey Levine, Stephen Tu, Nikolai Matni:
Multi-Task Imitation Learning for Linear Dynamical Systems. 586-599 - Srinath Tankasala, Mitch Pryor:
Accelerating Trajectory Generation for Quadrotors Using Transformers. 600-611 - Yaqi Duan, Martin J. Wainwright:
A finite-sample analysis of multi-step temporal difference estimates. 612-624 - Swaminathan Gurumurthy, Zachary Manchester, J. Zico Kolter:
Practical Critic Gradient based Actor Critic for On-Policy Reinforcement Learning. 625-638 - Swaminathan Gurumurthy, J. Zico Kolter, Zachary Manchester:
Deep Off-Policy Iterative Learning Control. 639-652 - Muhammad Abdullah Naeem, Miroslav Pajic:
Transportation-Inequalities, Lyapunov Stability and Sampling for Dynamical Systems on Continuous State Space. 653-664 - Prithvi Akella, Skylar X. Wei, Joel W. Burdick, Aaron D. Ames:
Learning Disturbances Online for Risk-Aware Control: Risk-Aware Flight with Less Than One Minute of Data. 665-678 - Cyrus Neary, Ufuk Topcu:
Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks. 679-691 - Yuyang Zhang, Runyu Zhang, Yuantao Gu, Na Li:
Multi-Agent Reinforcement Learning with Reward Delays. 692-704 - Wenliang Liu, Kevin Leahy, Zachary Serlin, Calin Belta:
CatlNet: Learning Communication and Coordination Policies from CaTL+ Specifications. 705-717 - Luigi Campanaro, Daniele De Martini, Siddhant Gangapurwala, Wolfgang Merkt, Ioannis Havoutis:
Roll-Drop: accounting for observation noise with a single parameter. 718-730 - Valentin Duruisseaux, Thai Duong, Melvin Leok, Nikolay Atanasov:
Lie Group Forced Variational Integrator Networks for Learning and Control of Robot Systems. 731-744 - Armand Comas Massague, Christian Fernandez Lopez, Sandesh Ghimire, Haolin Li, Mario Sznaier, Octavia I. Camps:
Learning Object-Centric Dynamic Modes from Video and Emerging Properties. 745-769 - Yuxiang Yang, Xiangyun Meng, Wenhao Yu, Tingnan Zhang, Jie Tan, Byron Boots:
Continuous Versatile Jumping Using Learned Action Residuals. 770-782 - Weiye Zhao, Tairan He, Changliu Liu:
Probabilistic Safeguard for Reinforcement Learning Using Safety Index Guided Gaussian Process Models. 783-796 - An T. Le, Kay Hansel, Jan Peters, Georgia Chalvatzaki:
Hierarchical Policy Blending As Optimal Transport. 797-812 - Xu Zhang, Marcos M. Vasconcelos:
Top-k data selection via distributed sample quantile inference. 813-824 - Harrison Delecki, Anthony Corso, Mykel J. Kochenderfer:
Model-based Validation as Probabilistic Inference. 825-837 - Tanya Veeravalli, Maxim Raginsky:
Nonlinear Controllability and Function Representation by Neural Stochastic Differential Equations. 838-850 - Saminda Abeyruwan, Alex Bewley, Nicholas Matthew Boffi, Krzysztof Marcin Choromanski, David B. D'Ambrosio, Deepali Jain, Pannag R. Sanketi, Anish Shankar, Vikas Sindhwani, Sumeet Singh, Jean-Jacques E. Slotine, Stephen Tu:
Agile Catching with Whole-Body MPC and Blackbox Policy Learning. 851-863 - Kehan Long, Yinzhuang Yi, Jorge Cortés, Nikolay Atanasov:
Distributionally Robust Lyapunov Function Search Under Uncertainty. 864-877 - Jan Achterhold
Black-Box vs. Gray-Box: A Case Study on Learning Table Tennis Ball Trajectory Prediction with Spin and Impacts. 878-890 - Adrien Banse, Licio Romao, Alessandro Abate, Raphaël M. Jungers:
Data-driven memory-dependent abstractions of dynamical systems. 891-902 - SooJean Han, Soon-Jo Chung, Johanna Gustafson:
Congestion Control of Vehicle Traffic Networks by Learning Structural and Temporal Patterns. 903-914 - Xiyu Deng, Christian Kurniawan
A Learning and Control Perspective for Microfinance. 915-927 - Reza Khodayi-mehr, Pingcheng Jian, Michael M. Zavlanos:
Physics-Guided Active Learning of Environmental Flow Fields. 928-940 - Francesco De Lellis, Marco Coraggio, Giovanni Russo, Mirco Musolesi, Mario di Bernardo:
CT-DQN: Control-Tutored Deep Reinforcement Learning. 941-953 - Panagiotis Vlantis, Leila Bridgeman, Michael M. Zavlanos:
Failing with Grace: Learning Neural Network Controllers that are Boundedly Unsafe. 954-965 - Joshua Pilipovsky, Vignesh Sivaramakrishnan, Meeko Oishi, Panagiotis Tsiotras:
Probabilistic Verification of ReLU Neural Networks via Characteristic Functions. 966-979 - Guanru Pan, Ruchuan Ou, Timm Faulwasser:
Data-driven Stochastic Output-Feedback Predictive Control: Recursive Feasibility through Interpolated Initial Conditions. 980-992 - Rishi Rani, Massimo Franceschetti:
Detection of Man-in-the-Middle Attacks in Model-Free Reinforcement Learning. 993-1007 - Zhaolin Ren, Yang Zheng, Maryam Fazel, Na Li:
On Controller Reduction in Linear Quadratic Gaussian Control with Performance Bounds. 1008-1019 - Deepan Muthirayan, Chinmay Maheshwari
Competing Bandits in Time Varying Matching Markets. 1020-1031 - Xinyi Chen, Edgar Minasyan, Jason D. Lee, Elad Hazan:
Regret Guarantees for Online Deep Control. 1032-1045 - Alex Devonport, Peter Seiler, Murat Arcak:
Frequency Domain Gaussian Process Models for H∞ Uncertainties. 1046-1057 - Sydney Dolan, Siddharth Nayak, Hamsa Balakrishnan:
Satellite Navigation and Coordination with Limited Information Sharing. 1058-1071 - Lukas Kesper, Sebastian Trimpe, Dominik Baumann:
Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control. 1072-1085 - Alessio Russo:
Analysis and Detectability of Offline Data Poisoning Attacks on Linear Dynamical Systems. 1086-1098 - Tsun-Hsuan Wang, Wei Xiao, Makram Chahine, Alexander Amini, Ramin M. Hasani, Daniela Rus:
Learning Stability Attention in Vision-based End-to-end Driving Policies. 1099-1111 - Arnob Ghosh:
Provably Efficient Model-free RL in Leader-Follower MDP with Linear Function Approximation. 1112-1124 - Kong Yao Chee, M. Ani Hsieh, Nikolai Matni:
Learning-enhanced Nonlinear Model Predictive Control using Knowledge-based Neural Ordinary Differential Equations and Deep Ensembles. 1125-1137 - Yingying Li, James A. Preiss, Na Li, Yiheng Lin, Adam Wierman, Jeff S. Shamma:
Online switching control with stability and regret guarantees. 1138-1151 - Elie Aljalbout, Maximilian Karl, Patrick van der Smagt:
CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces. 1152-1166 - Hancheng Min, Enrique Mallada:
Learning Coherent Clusters in Weakly-Connected Network Systems. 1167-1179 - Antoine Leeman, Johannes Köhler
Predictive safety filter using system level synthesis. 1180-1192 - Rahel Rickenbach, Elena Arcari, Melanie N. Zeilinger:
Time Dependent Inverse Optimal Control using Trigonometric Basis Functions. 1193-1204 - Daniel Tabas, Ahmed S. Zamzam, Baosen Zhang:
Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning. 1205-1217 - Majid Khadiv, Avadesh Meduri, Huaijiang Zhu, Ludovic Righetti, Bernhard Schölkopf:
Learning Locomotion Skills from MPC in Sensor Space. 1218-1230 - Sophia Huiwen Sun, Robin Walters, Jinxi Li, Rose Yu:
Probabilistic Symmetry for Multi-Agent Dynamics. 1231-1244 - Zifan Wang, Yulong Gao, Siyi Wang, Michael M. Zavlanos, Alessandro Abate, Karl Henrik Johansson:
Policy Evaluation in Distributional LQR. 1245-1256 - Nick-Marios T. Kokolakis, Kyriakos G. Vamvoudakis, Wassim M. Haddad:
Reachability Analysis-based Safety-Critical Control using Online Fixed-Time Reinforcement Learning. 1257-1270 - Tahiya Salam, Alice Kate Li, M. Ani Hsieh:
Online Estimation of the Koopman Operator Using Fourier Features. 1271-1283 - Tobias Enders, James Harrison, Marco Pavone, Maximilian Schiffer:
Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems. 1284-1296 - Doumitrou Daniil Nimara, Mohammadreza Malek-Mohammadi, Petter Ögren, Jieqiang Wei, Vincent Huang:
Model-Based Reinforcement Learning for Cavity Filter Tuning. 1297-1307 - Han Wang, Leonardo Felipe Toso, James Anderson:
FedSysID: A Federated Approach to Sample-Efficient System Identification. 1308-1320 - Patricia Pauli, Dennis Gramlich, Frank Allgöwer:
Lipschitz constant estimation for 1D convolutional neural networks. 1321-1332 - Hengquan Guo, Zhu Qi, Xin Liu:
Rectified Pessimistic-Optimistic Learning for Stochastic Continuum-armed Bandit with Constraints. 1333-1344 - Gautam Goel, Naman Agarwal, Karan Singh, Elad Hazan:
Best of Both Worlds in Online Control: Competitive Ratio and Policy Regret. 1345-1356 - Ian Char, Joseph Abbate, Laszlo Bardoczi, Mark D. Boyer, Youngseog Chung, Rory Conlin, Keith Erickson, Viraj Mehta, Nathan Richner, Egemen Kolemen, Jeff G. Schneider:
Offline Model-Based Reinforcement Learning for Tokamak Control. 1357-1372 - Tong Guanchun, Michael Muehlebach:
A Dynamical Systems Perspective on Discrete Optimization. 1373-1386 - Aritra Mitra, Hamed Hassani, George J. Pappas:
Linear Stochastic Bandits over a Bit-Constrained Channel. 1387-1399 - Yue Meng, Chuchu Fan:
Hybrid Systems Neural Control with Region-of-Attraction Planner. 1400-1415 - Killian Reed Wood, Emiliano Dall'Anese:
Online Saddle Point Tracking with Decision-Dependent Data. 1416-1428 - Bence Zsombor Hadlaczky, Noémi Friedman, Béla Takarics, Bálint Vanek:
Wing shape estimation with Extended Kalman filtering and KalmanNet neural network of a flexible wing aircraft. 1429-1440 - Baris Kayalibay, Atanas Mirchev, Ahmed Agha, Patrick van der Smagt, Justin Bayer:
Filter-Aware Model-Predictive Control. 1441-1454 - Alireza Farahmandi, Brian C. Reitz, Mark J. Debord, Douglas Philbrick, Katia Estabridis, Gary A. Hewer:
Hyperparameter Tuning of an Off-Policy Reinforcement Learning Algorithm for H∞ Tracking Control. 1455-1466 - Sourya Dey, Eric William Davis:
DLKoopman: A deep learning software package for Koopman theory. 1467-1479 - Michelle Guo, Yifeng Jiang, Andrew Everett Spielberg, Jiajun Wu, C. Karen Liu:
Benchmarking Rigid Body Contact Models. 1480-1492 - Kwangjun Ahn, Zakaria Mhammedi, Horia Mania, Zhang-Wei Hong, Ali Jadbabaie:
Model Predictive Control via On-Policy Imitation Learning. 1493-1505
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