L4DC 2023

114 papers

A Learning and Control Perspective for Microfinance Xiyu Deng, Christian Kurniawan, Adhiraj Chakraborty, Assane Gueye, Niangjun Chen, Yorie Nakahira

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A Reinforcement Learning Look at Risk-Sensitive Linear Quadratic Gaussian Control Leilei Cui, Tamer Basar, Zhong-Ping Jiang

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Accelerating Trajectory Generation for Quadrotors Using Transformers Srinath Tankasala, Mitch Pryor

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Adaptive Conformal Prediction for Motion Planning Among Dynamic Agents Anushri Dixit, Lars Lindemann, Skylar X Wei, Matthew Cleaveland, George J. Pappas, Joel W. Burdick

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Adaptive Regret for Control of Time-Varying Dynamics Paula Gradu, Elad Hazan, Edgar Minasyan

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Agile Catching with Whole-Body MPC and Blackbox Policy Learning 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 Slotine, Stephen Tu

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Analysis and Detectability of Offline Data Poisoning Attacks on Linear Dynamical Systems Alessio Russo

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Automated Reachability Analysis of Neural Network-Controlled Systems via Adaptive Polytopes Taha Entesari, Mahyar Fazlyab

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Automatic Integration for Fast and Interpretable Neural Point Processes Zihao Zhou, Rose Yu

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Benchmarking Rigid Body Contact Models Michelle Guo, Yifeng Jiang, Andrew Everett Spielberg, Jiajun Wu, Karen Liu

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Best of Both Worlds in Online Control: Competitive Ratio and Policy Regret Gautam Goel, Naman Agarwal, Karan Singh, Elad Hazan

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Black-Box vs. Gray-Box: A Case Study on Learning Table Tennis Ball Trajectory Prediction with Spin and Impacts Jan Achterhold, Philip Tobuschat, Hao Ma, Dieter Büchler, Michael Muehlebach, Joerg Stueckler

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Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control? Yi Tian, Kaiqing Zhang, Russ Tedrake, Suvrit Sra

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Can Learning Deteriorate Control? Analyzing Computational Delays in Gaussian Process-Based Event-Triggered Online Learning Xiaobing Dai, Armin Lederer, Zewen Yang, Sandra Hirche

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CatlNet: Learning Communication and Coordination Policies from CaTL+ Specifications Wenliang Liu, Kevin Leahy, Zachary Serlin, Calin Belta

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Certified Invertibility in Neural Networks via Mixed-Integer Programming Tianqi Cui, Thomas Bertalan, George J. Pappas, Manfred Morari, Yannis Kevrekidis, Mahyar Fazlyab

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CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces Elie Aljalbout, Maximilian Karl, Patrick van der Smagt

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Competing Bandits in Time Varying Matching Markets Deepan Muthirayan, Chinmay Maheshwari, Pramod Khargonekar, Shankar Sastry

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Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks Cyrus Neary, Ufuk Topcu

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Compositional Learning-Based Planning for Vision POMDPs Sampada Deglurkar, Michael H Lim, Johnathan Tucker, Zachary N Sunberg, Aleksandra Faust, Claire Tomlin

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Compositional Neural Certificates for Networked Dynamical Systems Songyuan Zhang, Yumeng Xiu, Guannan Qu, Chuchu Fan

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Concentration Phenomenon for Random Dynamical Systems: An Operator Theoretic Approach Muhammad Abdullah Naeem

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Congestion Control of Vehicle Traffic Networks by Learning Structural and Temporal Patterns SooJean Han, Soon-Jo Chung, Johanna Gustafson

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Continuous Versatile Jumping Using Learned Action Residuals Yuxiang Yang, Xiangyun Meng, Wenhao Yu, Tingnan Zhang, Jie Tan, Byron Boots

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Contrastive Example-Based Control Kyle Beltran Hatch, Benjamin Eysenbach, Rafael Rafailov, Tianhe Yu, Ruslan Salakhutdinov, Sergey Levine, Chelsea Finn

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CT-DQN: Control-Tutored Deep Reinforcement Learning Francesco De Lellis, Marco Coraggio, Giovanni Russo, Mirco Musolesi, Mario di Bernardo

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Data-Driven Memory-Dependent Abstractions of Dynamical Systems Adrien Banse, Licio Romao, Alessandro Abate, Raphael Jungers

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Data-Driven Stochastic Output-Feedback Predictive Control: Recursive Feasibility Through Interpolated Initial Conditions Guanru Pan, Ruchuan Ou, Timm Faulwasser

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Deep Off-Policy Iterative Learning Control Swaminathan Gurumurthy, J Zico Kolter, Zachary Manchester

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Designing System Level Synthesis Controllers for Nonlinear Systems with Stability Guarantees Lauren E Conger, Sydney Vernon, Eric Mazumdar

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Detection of Man-in-the-Middle Attacks in Model-Free Reinforcement Learning Rishi Rani, Massimo Franceschetti

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DiffTune$^+$: Hyperparameter-Free Auto-Tuning Using Auto-Differentiation Sheng Cheng, Lin Song, Minkyung Kim, Shenlong Wang, Naira Hovakimyan

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Distributionally Robust Lyapunov Function Search Under Uncertainty Kehan Long, Yinzhuang Yi, Jorge Cortes, Nikolay Atanasov

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DLKoopman: A Deep Learning Software Package for Koopman Theory Sourya Dey, Eric William Davis

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Efficient Reinforcement Learning Through Trajectory Generation Wenqi Cui, Linbin Huang, Weiwei Yang, Baosen Zhang

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End-to-End Learning to Warm-Start for Real-Time Quadratic Optimization Rajiv Sambharya, Georgina Hall, Brandon Amos, Bartolomeo Stellato

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Equilibria of Fully Decentralized Learning in Networked Systems Yan Jiang, Wenqi Cui, Baosen Zhang, Jorge Cortes

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Failing with Grace: Learning Neural Network Controllers That Are Boundedly Unsafe Panagiotis Vlantis, Leila Bridgeman, Michael Zavlanos

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FedSysID: A Federated Approach to Sample-Efficient System Identification Han Wang, Leonardo Felipe Toso, James Anderson

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Filter-Aware Model-Predictive Control Baris Kayalibay, Atanas Mirchev, Ahmed Agha, Patrick van der Smagt, Justin Bayer

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Frequency Domain Gaussian Process Models for $h^∞$ Uncertainties Alex Devonport, Peter Seiler, Murat Arcak

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Full Gradient Deep Reinforcement Learning for Average-Reward Criterion Tejas Pagare, Vivek Borkar, Konstantin Avrachenkov

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Guaranteed Conformance of Neurosymbolic Models to Natural Constraints Kaustubh Sridhar, Souradeep Dutta, James Weimer, Insup Lee

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Hierarchical Policy Blending as Optimal Transport An Thai Le, Kay Hansel, Jan Peters, Georgia Chalvatzaki

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Hybrid Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems Tobias Enders, James Harrison, Marco Pavone, Maximilian Schiffer

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Hybrid Systems Neural Control with Region-of-Attraction Planner Yue Meng, Chuchu Fan

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Hyperparameter Tuning of an Off-Policy Reinforcement Learning Algorithm for H∞ Tracking Control Alireza Farahmandi, Brian C Reitz, Mark Debord, Douglas Philbrick, Katia Estabridis, Gary Hewer

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Improving Gradient Computation for Differentiable Physics Simulation with Contacts Yaofeng Desmond Zhong, Jiequn Han, Biswadip Dey, Georgia Olympia Brikis

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In-Distribution Barrier Functions: Self-Supervised Policy Filters That Avoid Out-of-Distribution States Fernando Castañeda, Haruki Nishimura, Rowan Thomas McAllister, Koushil Sreenath, Adrien Gaidon

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Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning Daniel Tabas, Ahmed S Zamzam, Baosen Zhang

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Interval Reachability of Nonlinear Dynamical Systems with Neural Network Controllers Saber Jafarpour, Akash Harapanahalli, Samuel Coogan

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ISAACS: Iterative Soft Adversarial Actor-Critic for Safety Kai-Chieh Hsu, Duy Phuong Nguyen, Jaime Fernàndez Fisac

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Learning Coherent Clusters in Weakly-Connected Network Systems Hancheng Min, Enrique Mallada

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Learning Disturbances Online for Risk-Aware Control: Risk-Aware Flight with Less than One Minute of Data Prithvi Akella, Skylar X Wei, Joel W. Burdick, Aaron Ames

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Learning Locomotion Skills from MPC in Sensor Space Majid Khadiv, Avadesh Meduri, Huaijiang Zhu, Ludovic Righetti, Bernhard Schölkopf

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Learning Object-Centric Dynamic Modes from Video and Emerging Properties Armand Comas, Christian Fernandez Lopez, Sandesh Ghimire, Haolin Li, Mario Sznaier, Octavia Camps

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Learning on Manifolds: Universal Approximations Properties Using Geometric Controllability Conditions for Neural ODEs Karthik Elamvazhuthi, Xuechen Zhang, Samet Oymak, Fabio Pasqualetti

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Learning Policy-Aware Models for Model-Based Reinforcement Learning via Transition Occupancy Matching Yecheng Jason Ma, Kausik Sivakumar, Jason Yan, Osbert Bastani, Dinesh Jayaraman

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Learning Robust State Observers Using Neural ODEs Keyan Miao, Konstantinos Gatsis

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Learning Stability Attention in Vision-Based End-to-End Driving Policies Tsun-Hsuan Wang, Wei Xiao, Makram Chahine, Alexander Amini, Ramin Hasani, Daniela Rus

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Learning the Dynamics of Autonomous Nonlinear Delay Systems Xunbi Ji, Gabor Orosz

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Learning Trust over Directed Graphs in Multiagent Systems Orhan Eren Akgun, Arif Kerem Dayi, Stephanie Gil, Angelia Nedich

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Learning-Enhanced Nonlinear Model Predictive Control Using Knowledge-Based Neural Ordinary Differential Equations and Deep Ensembles Kong Yao Chee, M. Ani Hsieh, Nikolai Matni

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Learning-to-Learn to Guide Random Search: Derivative-Free Meta Blackbox Optimization on Manifold Bilgehan Sel, Ahmad Tawaha, Yuhao Ding, Ruoxi Jia, Bo Ji, Javad Lavaei, Ming Jin

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Lie Group Forced Variational Integrator Networks for Learning and Control of Robot Systems Valentin Duruisseaux, Thai P. Duong, Melvin Leok, Nikolay Atanasov

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Linear Stochastic Bandits over a Bit-Constrained Channel Aritra Mitra, Hamed Hassani, George J. Pappas

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Lipschitz Constant Estimation for 1d Convolutional Neural Networks Patricia Pauli, Dennis Gramlich, Frank Allgöwer

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Model Predictive Control via On-Policy Imitation Learning Kwangjun Ahn, Zakaria Mhammedi, Horia Mania, Zhang-Wei Hong, Ali Jadbabaie

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Model-Based Reinforcement Learning for Cavity Filter Tuning Doumitrou Daniil Nimara, Mohammadreza Malek-Mohammadi, Petter Ogren, Jieqiang Wei, Vincent Huang

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Model-Based Validation as Probabilistic Inference Harrison Delecki, Anthony Corso, Mykel Kochenderfer

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Modified Policy Iteration for Exponential Cost Risk Sensitive MDPs Yashaswini Murthy, Mehrdad Moharrami, R. Srikant

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Multi-Agent Reinforcement Learning with Reward Delays Yuyang Zhang, Runyu Zhang, Yuantao Gu, Na Li

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Multi-Task Imitation Learning for Linear Dynamical Systems Thomas T. Zhang, Katie Kang, Bruce D Lee, Claire Tomlin, Sergey Levine, Stephen Tu, Nikolai Matni

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Nonlinear Controllability and Function Representation by Neural Stochastic Differential Equations Tanya Veeravalli, Maxim Raginsky

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Offline Model-Based Reinforcement Learning for Tokamak Control Ian Char, Joseph Abbate, Laszlo Bardoczi, Mark Boyer, Youngseog Chung, Rory Conlin, Keith Erickson, Viraj Mehta, Nathan Richner, Egemen Kolemen, Jeff Schneider

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On Controller Reduction in Linear Quadratic Gaussian Control with Performance Bounds Zhaolin Ren, Yang Zheng, Maryam Fazel, Na Li

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Online Estimation of the Koopman Operator Using Fourier Features Tahiya Salam, Alice Kate Li, M. Ani Hsieh

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Online Saddle Point Tracking with Decision-Dependent Data Killian Reed Wood, Emiliano Dall’Anese

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Online Switching Control with Stability and Regret Guarantees Yingying Li, James A Preiss, Na Li, Yiheng Lin, Adam Wierman, Jeff S Shamma

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Operator Learning for Nonlinear Adaptive Control Luke Bhan, Yuanyuan Shi, Miroslav Krstic

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Physics-Enhanced Gaussian Process Variational Autoencoder Thomas Beckers, Qirui Wu, George J. Pappas

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Physics-Guided Active Learning of Environmental Flow Fields Reza Khodayi-mehr, Pingcheng Jian, Michael M. Zavlanos

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Physics-Informed Model-Based Reinforcement Learning Adithya Ramesh, Balaraman Ravindran

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Policy Evaluation in Distributional LQR Zifan Wang, Yulong Gao, Siyi Wang, Michael M. Zavlanos, Alessandro Abate, Karl Henrik Johansson

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Policy Gradient Play with Networked Agents in Markov Potential Games Sarper Aydin, Ceyhun Eksin

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Policy Learning for Active Target Tracking over Continuous $SE(3)$ Trajectories Pengzhi Yang, Shumon Koga, Arash Asgharivaskasi, Nikolay Atanasov

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Practical Critic Gradient Based Actor Critic for On-Policy Reinforcement Learning Swaminathan Gurumurthy, Zachary Manchester, J Zico Kolter

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Predictive Safety Filter Using System Level Synthesis Antoine Leeman, Johannes Köhler, Samir Bennani, Melanie Zeilinger

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Probabilistic Invariance for Gaussian Process State Space Models Paul Griffioen, Alex Devonport, Murat Arcak

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Probabilistic Safeguard for Reinforcement Learning Using Safety Index Guided Gaussian Process Models Weiye Zhao, Tairan He, Changliu Liu

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Probabilistic Symmetry for Multi-Agent Dynamics Sophia Huiwen Sun, Robin Walters, Jinxi Li, Rose Yu

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Probabilistic Verification of ReLU Neural Networks via Characteristic Functions Joshua Pilipovsky, Vignesh Sivaramakrishnan, Meeko Oishi, Panagiotis Tsiotras

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Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning Dongsheng Ding, Xiaohan Wei, Zhuoran Yang, Zhaoran Wang, Mihailo Jovanovic

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Provably Efficient Model-Free RL in Leader-Follower MDP with Linear Function Approximation Arnob Ghosh

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Reachability Analysis-Based Safety-Critical Control Using Online Fixed-Time Reinforcement Learning Nick-Marios Kokolakis, Kyriakos G Vamvoudakis, Wassim Haddad

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Rectified Pessimistic-Optimistic Learning for Stochastic Continuum-Armed Bandit with Constraints Hengquan Guo, Zhu Qi, Xin Liu

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Regret Analysis of Online LQR Control via Trajectory Prediction and Tracking Yitian Chen, Timothy L Molloy, Tyler Summers, Iman Shames

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Regret Guarantees for Online Deep Control Xinyi Chen, Edgar Minasyan, Jason D. Lee, Elad Hazan

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Roll-Drop: Accounting for Observation Noise with a Single Parameter Luigi Campanaro, Daniele De Martini, Siddhant Gangapurwala, Wolfgang Merkt, Ioannis Havoutis

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Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions Yikun Cheng, Pan Zhao, Naira Hovakimyan

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Sample Complexity Bound for Evaluating the Robust Observer’s Performance Under Coprime Factors Uncertainty Serban Sabau, Yifei Zhang, Sourav Kumar Ukil

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Satellite Navigation and Coordination with Limited Information Sharing Sydney Dolan, Siddharth Nayak, Hamsa Balakrishnan

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Targeted Adversarial Attacks Against Neural Network Trajectory Predictors Kaiyuan Tan, Jun Wang, Yiannis Kantaros

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Template-Based Piecewise Affine Regression Guillaume O Berger, Sriram Sankaranarayanan

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The Impact of the Geometric Properties of the Constraint Set in Safe Optimization with Bandit Feedback Spencer Hutchinson, Berkay Turan, Mahnoosh Alizadeh

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Time Dependent Inverse Optimal Control Using Trigonometric Basis Functions Rahel Rickenbach, Elena Arcari, Melanie Zeilinger

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Time-Incremental Learning of Temporal Logic Classifiers Using Decision Trees Erfan Aasi, Mingyu Cai, Cristian Ioan Vasile, Calin Belta

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Top-K Data Selection via Distributed Sample Quantile Inference Xu Zhang, Marcos M. Vasconcelos

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Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control Lukas Kesper, Sebastian Trimpe, Dominik Baumann

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Transportation-Inequalities, Lyapunov Stability and Sampling for Dynamical Systems on Continuous State Space Muhammad Abdullah Naeem, Miroslav Pajic

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Wing Shape Estimation with Extended Kalman Filtering and KalmanNet Neural Network of a Flexible Wing Aircraft Bence Zsombor Hadlaczky, Noémi Friedman, Béla Takarics, Balint Vanek

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