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ORCID6th L4DC 2024: Oxford, UK
- Alessandro Abate, Mark Cannon, Kostas Margellos, Antonis Papachristodoulou:
6th Annual Learning for Dynamics & Control Conference, 15-17 July 2024, University of Oxford, Oxford, UK. Proceedings of Machine Learning Research 242, PMLR 2024 - Paula X. Chen, Tingwei Meng, Zongren Zou, Jérôme Darbon, George Em Karniadakis:
Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning. 1-12 - Achkan Salehi, Stéphane Doncieux:
Data-efficient, explainable and safe box manipulation: Illustrating the advantages of physical priors in model-predictive control. 13-24 - Yihang Yao, Zuxin Liu, Zhepeng Cen, Peide Huang, Tingnan Zhang, Wenhao Yu, Ding Zhao:
Gradient shaping for multi-constraint safe reinforcement learning. 25-39 - Abdullah Akgül, Gozde Unal, Melih Kandemir:
Continual learning of multi-modal dynamics with external memory. 40-51 - Songyuan Zhang, Chuchu Fan:
Learning to stabilize high-dimensional unknown systems using Lyapunov-guided exploration. 52-67 - Brett Barkley, Amy Zhang, David Fridovich-Keil:
An investigation of time reversal symmetry in reinforcement learning. 68-79 - Rui Yan, Gabriel Santos, Gethin Norman, David Parker, Marta Kwiatkowska:
HSVI-based online minimax strategies for partially observable stochastic games with neural perception mechanisms. 80-91 - Hanjiang Hu, Jianglin Lan, Changliu Liu:
Real-time safe control of neural network dynamic models with sound approximation. 92-103 - Emma Cramer, Jonas Reiher, Sebastian Trimpe:
Tracking object positions in reinforcement learning: A metric for keypoint detection. 104-116 - Andreas Hinderyckx, Florence Guillaume:
Linearised data-driven LSTM-based control of multi-input HVAC systems. 117-129 - Ivan Markovsky:
The behavioral toolbox. 130-141 - Chenguang Zhao, Huan Yu:
Learning "Look-Ahead" Nonlocal Traffic Dynamics in a Ring Road. 142-154 - Berkay Turan, Spencer Hutchinson, Mahnoosh Alizadeh:
Safe dynamic pricing for nonstationary network resource allocation. 155-167 - Spencer Hutchinson, Mahnoosh Alizadeh:
Safe online convex optimization with multi-point feedback. 168-180 - Xiangyuan Zhang, Weichao Mao, Saviz Mowlavi, Mouhacine Benosman, Tamer Basar:
Controlgym: Large-scale control environments for benchmarking reinforcement learning algorithms. 181-196 - Puya Latafat, Andreas Themelis, Panagiotis Patrinos:
On the convergence of adaptive first order methods: proximal gradient and alternating minimization algorithms. 197-208 - Carl R. Richardson, Matthew C. Turner, Steve R. Gunn, Ross Drummond:
Strengthened stability analysis of discrete-time Lurie systems involving ReLU neural networks. 209-221 - Patrick Henkel, Tobias Kasperski, Phillip Stoffel, Dirk Müller:
Interpretable data-driven model predictive control of building energy systems using SHAP. 222-234 - Philipp Pilar, Niklas Wahlström:
Physics-informed neural networks with unknown measurement noise. 235-247 - Naram Mhaisen, George Iosifidis:
Adaptive online non-stochastic control. 248-259 - Heiko Hoppe, Tobias Enders, Quentin Cappart, Maximilian Schiffer:
Global rewards in multi-agent deep reinforcement learning for autonomous mobility on demand systems. 260-272 - Tanmay Gautam, Reid Pryzant, Ziyi Yang, Chenguang Zhu, Somayeh Sojoudi:
Soft convex quantization: revisiting Vector Quantization with convex optimization. 273-285 - Yukai Tang, Jean-Bernard Lasserre, Heng Yang:
Uncertainty quantification of set-membership estimation in control and perception: Revisiting the minimum enclosing ellipsoid. 286-298 - Olle Kjellqvist:
Minimax dual control with finite-dimensional information state. 299-311 - Mohammad Alsalti, Victor G. Lopez, Matthias Albrecht Müller:
An efficient data-based off-policy Q-learning algorithm for optimal output feedback control of linear systems. 312-323 - Weiyao Wang, Xinyuan Fang, Gregory D. Hager:
Adapting Image-based RL Policies via Predicted Rewards. 324-336 - Dieter Teichrib, Moritz Schulze Darup:
Piecewise regression via mixed-integer programming for MPC. 337-348 - Henrik Hose, Alexander Gräfe, Sebastian Trimpe:
Parameter-adaptive approximate MPC: Tuning neural-network controllers without retraining. 349-360 - Weichao Mao, Haoran Qiu, Chen Wang, Hubertus Franke, Zbigniew Kalbarczyk, Tamer Basar:
$\widetilde{O}(T^{-1})$ {C}onvergence to (coarse) correlated equilibria in full-information general-sum markov games. 361-374 - Rahel Rickenbach, Anna Scampicchio, Melanie N. Zeilinger:
Inverse optimal control as an errors-in-variables problem. 375-386 - Nicolas Chatzikiriakos
, Kim Peter Wabersich, Felix Berkel, Patricia Pauli, Andrea Iannelli:
Learning soft constrained MPC value functions: Efficient MPC design and implementation providing stability and safety guarantees. 387-398 - Yiwen Lu, Zishuo Li, Yihan Zhou, Na Li, Yilin Mo:
MPC-inspired reinforcement learning for verifiable model-free control. 399-413 - Mohak Bhardwaj, Thomas Lampe, Michael Neunert, Francesco Romano, Abbas Abdolmaleki, Arunkumar Byravan, Markus Wulfmeier, Martin A. Riedmiller, Jonas Buchli:
Real-world fluid directed rigid body control via deep reinforcement learning. 414-427 - Zetong Xuan, Alper Kamil Bozkurt, Miroslav Pajic, Yu Wang:
On the uniqueness of solution for the Bellman equation of LTL objectives. 428-439 - Tara Toufighi, Minh Bui, Rakesh Shrestha, Mo Chen:
Decision boundary learning for safe vision-based navigation via Hamilton-Jacobi reachability analysis and support vector machine. 440-452 - Tao Wang, Bo Zhao, Sicun Gao, Rose Yu:
Understanding the difficulty of solving Cauchy problems with PINNs. 453-465 - Motoya Ohnishi, Iretiayo Akinola, Jie Xu, Ajay Mandlekar, Fabio Ramos:
Signatures meet dynamic programming: Generalizing Bellman equations for trajectory following. 466-479 - Vijeth Hebbar, Cedric Langbort:
Online decision making with history-average dependent costs. 480-491 - Yulong Gao, Shuhao Yan, Jian Zhou, Mark Cannon, Alessandro Abate, Karl Henrik Johansson:
Learning-based rigid tube model predictive control. 492-503 - Anders Rantzer:
A data-driven Riccati equation. 504-513 - Elizabeth Dietrich, Alex Devonport, Murat Arcak:
Nonconvex scenario optimization for data-driven reachability. 514-527 - Kong Yao Chee, Thales C. Silva, M. Ani Hsieh, George J. Pappas:
Uncertainty quantification and robustification of model-based controllers using conformal prediction. 528-540 - Tim Salzmann, Jon Arrizabalaga, Joel Andersson, Marco Pavone, Markus Ryll:
Learning for CasADi: Data-driven models in numerical optimization. 541-553 - Yihuai Zhang, Ruiguo Zhong, Huan Yu:
Neural operators for boundary stabilization of stop-and-go traffic. 554-565 - Jayanth Bhargav, Mahsa Ghasemi, Shreyas Sundaram:
Submodular information selection for hypothesis testing with misclassification penalties. 566-577 - Luigi Campanaro, Siddhant Gangapurwala, Wolfgang Merkt, Ioannis Havoutis:
Learning and deploying robust locomotion policies with minimal dynamics randomization. 578-590 - Miguel Aguiar, Amritam Das, Karl Henrik Johansson:
Learning flow functions of spiking systems. 591-602 - Johannes Buerger, Mark Cannon, Martin Doff-Sotta:
Safe learning in nonlinear model predictive control. 603-614 - Jun Yamada, Jack Collins, Ingmar Posner:
Efficient skill acquisition for insertion tasks in obstructed environments. 615-627 - Zhen Tian, Dezong Zhao, Zhihao Lin, David Flynn, Wenjing Zhao, Daxin Tian:
Balanced reward-inspired reinforcement learning for autonomous vehicle racing. 628-640 - Zhengfei Zhang, Yunyue Wei, Yanan Sui:
An invariant information geometric method for high-dimensional online optimization. 641-653 - Haoyu Han, Heng Yang:
On the nonsmooth geometry and neural approximation of the optimal value function of infinite-horizon pendulum swing-up. 654-666 - Joshua Pilipovsky, Panagiotis Tsiotras:
Data-driven robust covariance control for uncertain linear systems. 667-678 - Junxuan Shen, Adam Wierman, Guannan Qu:
Combining model-based controller and ML advice via convex reparameterization. 679-693 - Noel Brindise, Andres Felipe Posada-Moreno, Cedric Langbort, Sebastian Trimpe:
Pointwise-in-time diagnostics for reinforcement learning during training and runtime. 694-706 - Tianyue Zhou, Jung-Hoon Cho, Babak Rahimi Ardabili, Hamed Tabkhi, Cathy Wu:
Expert with Clustering: Hierarchical Online Preference Learning Framework. 707-718 - Albert Lin, Somil Bansal:
Verification of neural reachable tubes via scenario optimization and conformal prediction. 719-731 - Kimia Kazemian, Yahya Sattar, Sarah Dean:
Random features approximation for control-affine systems. 732-744 - Marsalis T. Gibson, David Babazadeh, Claire J. Tomlin, S. Shankar Sastry:
Hacking predictors means hacking cars: Using sensitivity analysis to identify trajectory prediction vulnerabilities for autonomous driving security. 745-757 - Joshua Hanson, Maxim Raginsky:
Rademacher complexity of neural ODEs via Chen-Fliess series. 758-769 - Muhammad Aneeq uz Zaman, Mathieu Laurière, Alec Koppel, Tamer Basar:
Robust cooperative multi-agent reinforcement learning: A mean-field type game perspective. 770-783 - Tiancheng Qin, S. Rasoul Etesami:
Learning ε-Nash equilibrium stationary policies in stochastic games with unknown independent chains using online mirror descent. 784-795 - Samarth Gupta, Saurabh Amin:
Uncertainty informed optimal resource allocation with Gaussian process based Bayesian inference. 796-812 - Zeji Yi, Yunyue Wei, Chu Xin Cheng, Kaibo He, Yanan Sui:
Improving sample efficiency of high dimensional Bayesian optimization with MCMC. 813-824 - Padmanaba Srinivasan, William J. Knottenbelt:
SpOiLer: Offline reinforcement learning using scaled penalties. 825-838 - Jannis O. Lübsen, Christian Hespe, Annika Eichler:
Towards safe multi-task Bayesian optimization. 839-851 - Yatong Bai, Brendon G. Anderson, Somayeh Sojoudi:
Mixing classifiers to alleviate the accuracy-robustness trade-off. 852-865 - Satheesh Thangavel, Rathinasamy Sakthivel:
Design of observer-based finite-time control for inductively coupled power transfer system with random gain fluctuations. 866-875 - Luke Rickard, Alessandro Abate, Kostas Margellos:
Learning robust policies for uncertain parametric Markov decision processes. 876-889 - Xiangyu Mao, Jianping He, Chengpu Yu, Chongrong Fang:
Conditions for parameter unidentifiability of linear ARX systems for enhancing security. 890-901 - Leonardo Felipe Toso, Donglin Zhan, James Anderson, Han Wang:
Meta-learning linear quadratic regulators: A policy gradient MAML approach for model-free LQR. 902-915 - Wouter Jongeneel, Daniel Kuhn, Mengmeng Li:
A large deviations perspective on policy gradient algorithms. 916-928 - Johan Peralez, Madiha Nadri:
Deep model-free KKL observer: A switching approach. 929-940 - Sara Maria Brancato, Davide Salzano, Francesco De Lellis, Davide Fiore, Giovanni Russo, Mario di Bernardo:
In vivo learning-based control of microbial populations density in bioreactors. 941-953 - Daniel Jarne Ornia, Licio Romao, Lewis Hammond, Manuel Mazo Jr., Alessandro Abate:
Bounded robustness in reinforcement learning via lexicographic objectives. 954-967 - Xiao Li, Yutong Li, Anouck Girard, Ilya V. Kolmanovsky:
System-level safety guard: Safe tracking control through uncertain neural network dynamics models. 968-979 - Bruce D. Lee, Anders Rantzer, Nikolai Matni:
Nonasymptotic regret analysis of adaptive linear quadratic control with model misspecification. 980-992 - Feng-Yi Liao, Lijun Ding, Yang Zheng:
Error bounds, PL condition, and quadratic growth for weakly convex functions, and linear convergences of proximal point methods. 993-1005 - Hyun Joe Jeong, Zheng Gong, Somil Bansal, Sylvia L. Herbert:
Parameterized fast and safe tracking (FaSTrack) using DeepReach. 1006-1017 - Amon Lahr, Filip Tronarp, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig, Melanie N. Zeilinger:
Probabilistic ODE solvers for integration error-aware numerical optimal control. 1018-1032 - Antonia Holzapfel, Paul Brunzema, Sebastian Trimpe:
Event-triggered safe Bayesian optimization on quadcopters. 1033-1045 - Yitao Bai, Thinh T. Doan:
Finite-time complexity of incremental policy gradient methods for solving multi-task reinforcement learning. 1046-1057 - Riccardo Zuliani, Raffaele Soloperto, John Lygeros:
Convergence guarantees for adaptive model predictive control with kinky inference. 1058-1070 - Xu Shang, Yang Zheng:
Convex approximations for a bi-level formulation of data-enabled predictive control. 1071-1082 - Luke Bhan, Yuexin Bian, Miroslav Krstic, Yuanyuan Shi:
PDE control gym: A benchmark for data-driven boundary control of partial differential equations. 1083-1095 - Yikang Wang, Adolfo Perrusquía, Dmitry I. Ignatyev:
Towards bio-inspired control of aerial vehicle: Distributed aerodynamic parameters for state prediction. 1096-1106 - Mohammadreza Nakhaei, Aidan Scannell, Joni Pajarinen:
Residual learning and context encoding for adaptive offline-to-online reinforcement learning. 1107-1121 - Zeji Yi, Chaoyi Pan, Guanqi He, Guannan Qu, Guanya Shi:
CoVO-MPC: Theoretical analysis of sampling-based MPC and optimal covariance design. 1122-1135 - Bing Song, Jean-Jacques E. Slotine, Quang-Cuong Pham:
Stable modular control via contraction theory for reinforcement learning. 1136-1148 - Wentao Tang:
Data-driven bifurcation analysis via learning of homeomorphism. 1149-1160 - Nolan Fey, He Li, Nicholas Adrian, Patrick M. Wensing, Michael D. Lemmon:
A learning-based framework to adapt legged robots on-the-fly to unexpected disturbances. 1161-1173 - Jaeuk Shin, Giho Kim, Howon Lee, Joonho Han, Insoon Yang:
On task-relevant loss functions in meta-reinforcement learning. 1174-1186 - Simon Sinong Zhan, Yixuan Wang, Qingyuan Wu, Ruochen Jiao, Chao Huang, Qi Zhu:
State-wise safe reinforcement learning with pixel observations. 1187-1201 - Leopoldo Agorio, Sean Van Alen, Miguel Calvo-Fullana, Santiago Paternain, Juan Andrés Bazerque:
Multi-agent assignment via state augmented reinforcement learning. 1202-1213 - Jasper Hoffmann, Diego Fernandez Clausen, Julien Brosseit, Julian Bernhard, Klemens Esterle, Moritz Werling, Michael Karg, Joschka Bödecker:
PlanNetX: Learning an efficient neural network planner from MPC for longitudinal control. 1214-1227 - Ross Drummond, Pablo R. Baldivieso, Giorgio Valmorbida:
Mapping back and forth between model predictive control and neural networks. 1228-1240 - Aneesh Raghavan, Karl Henrik Johansson:
A multi-modal distributed learning algorithm in reproducing kernel Hilbert spaces. 1241-1252 - Aritra Mitra, Lintao Ye, Vijay Gupta:
Towards model-free LQR control over rate-limited channels. 1253-1265 - Mohamad Louai Shehab, Antoine Aspeel, Nikos Aréchiga, Andrew Best, Necmiye Ozay:
Learning true objectives: Linear algebraic characterizations of identifiability in inverse reinforcement learning. 1266-1277 - Will Lavanakul, Jason J. Choi, Koushil Sreenath, Claire J. Tomlin:
Safety filters for black-box dynamical systems by learning discriminating hyperplanes. 1278-1291 - Giulio Giacomuzzo, Riccardo Cescon, Diego Romeres, Ruggero Carli, Alberto Dalla Libera:
Lagrangian inspired polynomial estimator for black-box learning and control of underactuated systems. 1292-1304 - Mohammad Bajelani, Klaske van Heusden:
From raw data to safety: Reducing conservatism by set expansion. 1305-1317 - Daniel Felipe Ordoñez Apraez, Vladimir Kostic, Giulio Turrisi, Pietro Novelli, Carlos Mastalli, Claudio Semini, Massimiliano Pontil:
Dynamics harmonic analysis of robotic systems: Application in data-driven Koopman modelling. 1318-1329 - Charis J. Stamouli, Lars Lindemann, George J. Pappas:
Recursively feasible shrinking-horizon MPC in dynamic environments with conformal prediction guarantees. 1330-1342 - Renukanandan Tumu, Matthew Cleaveland, Rahul Mangharam, George J. Pappas, Lars Lindemann:
Multi-modal conformal prediction regions by optimizing convex shape templates. 1343-1356 - Beomseok Kang, Harshit Kumar, Minah Lee, Biswadeep Chakraborty, Saibal Mukhopadhyay:
Learning locally interacting discrete dynamical systems: Towards data-efficient and scalable prediction. 1357-1369 - Zhenjiang Mao, Carson Sobolewski, Ivan Ruchkin:
How safe am I given what I see? Calibrated prediction of safety chances for image-controlled autonomy. 1370-1387 - Ross Drummond, Chris Guiver, Matthew C. Turner:
Convex neural network synthesis for robustness in the 1-norm. 1388-1399 - Rémy Hosseinkhan Boucher, Stella Douka, Onofrio Semeraro, Lionel Mathelin:
Increasing information for model predictive control with semi-Markov decision processes. 1400-1414 - Rui Dai, Giulio Evangelisti, Sandra Hirche:
Physically consistent modeling & identification of nonlinear friction with dissipative Gaussian processes. 1415-1426 - Hemant Kumawat, Biswadeep Chakraborty, Saibal Mukhopadhyay:
STEMFold: Stochastic temporal manifold for multi-agent interactions in the presence of hidden agents. 1427-1439 - Shayan Meshkat Alsadat, Nasim Baharisangari, Zhe Xu:
Distributed on-the-fly control of multi-agent systems with unknown dynamics: Using limited data to obtain near-optimal control. 1440-1451 - Elisa Alboni, Gianluigi Grandesso, Gastone Pietro Rosati Papini, Justin Carpentier, Andrea Del Prete:
CACTO-SL: Using Sobolev learning to improve continuous actor-critic with trajectory optimization. 1452-1463 - Runyu Zhang, Haitong Ma, Na Li:
Multi-agent coverage control with transient behavior consideration. 1464-1476 - Amy K. Strong, Leila Jasmine Bridgeman:
Data driven verification of positive invariant sets for discrete, nonlinear systems. 1477-1488 - Emma Clark, Kanghyun Ryu, Negar Mehr:
Adaptive teaching in heterogeneous agents: Balancing surprise in sparse reward scenarios. 1489-1501 - Gautam Goel, Peter L. Bartlett:
Can a transformer represent a Kalman filter? 1502-1512 - Sophia Huiwen Sun, Wenyuan Chen, Zihao Zhou, Sonia Fereidooni, Elise Jortberg, Rose Yu:
Data-driven simulator for mechanical circulatory support with domain adversarial neural process. 1513-1525 - Vivian Lin, Kuk Jin Jang, Souradeep Dutta, Michele Caprio, Oleg Sokolsky, Insup Lee:
DC4L: Distribution shift recovery via data-driven control for deep learning models. 1526-1538 - Sihan Zeng, Youngdae Kim, Yuxuan Ren, Kibaek Kim:
QCQP-Net: Reliably learning feasible alternating current optimal power flow solutions under constraints. 1539-1551 - Riccardo Brumali, Guido Carnevale, Giuseppe Notarstefano:
A deep learning approach for distributed aggregative optimization with users' feedback. 1552-1564 - Christopher Strong, Kaylene C. Stocking, Jingqi Li, Tianjiao Zhang, Jack L. Gallant, Claire J. Tomlin:
A framework for evaluating human driver models using neuroimaging. 1565-1578 - Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang, Michael G. Forbes, R. Bhushan Gopaluni:
Deep Hankel matrices with random elements. 1579-1591 - Hao-Lun Hsu, Miroslav Pajic:
Robust exploration with adversary via Langevin Monte Carlo. 1592-1605 - Weiqin Chen, Santiago Paternain:
Generalized constraint for probabilistic safe reinforcement learning. 1606-1618 - Paul Brunzema, Paul Kruse, Sebastian Trimpe:
Neural processes with event triggers for fast adaptation to changes. 1619-1632 - Ibon Gracia, Dimitris Boskos, Luca Laurenti, Morteza Lahijanian:
Data-driven strategy synthesis for stochastic systems with unknown nonlinear disturbances. 1633-1645 - Tim Seyde, Peter Werner, Wilko Schwarting, Markus Wulfmeier, Daniela Rus:
Growing Q-networks: Solving continuous control tasks with adaptive control resolution. 1646-1661 - Christine Allen-Blanchette:
Hamiltonian GAN. 1662-1674 - Sean Vaskov, Wilko Schwarting, Chris L. Baker:
Do no harm: A counterfactual approach to safe reinforcement learning. 1675-1687 - Taylan Kargin, Joudi Hajar, Vikrant Malik, Babak Hassibi:
Wasserstein distributionally robust regret-optimal control over infinite-horizon. 1688-1701 - George Pantazis, Filiberto Fele, Filippo Fabiani, Sergio Grammatico, Kostas Margellos:
Probably approximately correct stability of allocations in uncertain coalitional games with private sampling. 1702-1714 - Jack Naish, Jacob Rodriguez, Jenny Zhang, Bryson Jones, Guglielmo Daddi, Andrew L. Orekhov, Rob Royce, Michael Paton, Howie Choset, Masahiro Ono, Rohan Thakker:
Reinforcement learning-driven parametric curve fitting for snake robot gait design. 1715-1727 - Lei Zhang, Mukesh Ghimire, Zhe Xu, Wenlong Zhang, Yi Ren:
Pontryagin neural operator for solving general-sum differential games with parametric state constraints. 1728-1740 - Lilli Frison, Simon Gölzhäuser:
Adaptive neural network based control approach for building energy control under changing environmental conditions. 1741-1752 - Kaiyuan Tan, Peilun Li, Thomas Beckers:
Physics-constrained learning for PDE systems with uncertainty quantified port-Hamiltonian models. 1753-1764 - Yuliang Gu, Sheng Cheng, Naira Hovakimyan:
Proto-MPC: An encoder-prototype-decoder approach for quadrotor control in challenging winds. 1765-1776 - Victor Kolev, Rafael Rafailov, Kyle Hatch, Jiajun Wu, Chelsea Finn:
Efficient imitation learning with conservative world models. 1777-1790 - Jonathan Gornet, Bruno Sinopoli:
Restless bandits with rewards generated by a linear Gaussian dynamical system. 1791-1802
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last updated on 2024-11-08 20:17 CET by the dblp team
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