L4DC 2023

114 papers

Model Predictive Control via On-Policy Imitation Learning

Kwangjun Ahn, Zakaria Mhammedi, Horia Mania, Zhang-Wei Hong, Ali Jadbabaie

Benchmarking Rigid Body Contact Models

Michelle Guo, Yifeng Jiang, Andrew Everett Spielberg, Jiajun Wu, Karen Liu

DLKoopman: A deep learning software package for Koopman theory

Sourya Dey, Eric William Davis

Hyperparameter Tuning of an Off-Policy Reinforcement Learning Algorithm for H∞ Tracking Control

Alireza Farahmandi, Brian C Reitz, Mark Debord, Douglas Philbrick, Katia Estabridis

Filter-Aware Model-Predictive Control

Baris Kayalibay, Atanas Mirchev, Ahmed Agha, Patrick van der Smagt, Justin Bayer

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

Online Saddle Point Tracking with Decision-Dependent Data

Killian Reed Wood, Emiliano Dall’Anese

Hybrid Systems Neural Control with Region-of-Attraction Planner

Yue Meng, Chuchu Fan

Linear Stochastic Bandits over a Bit-Constrained Channel

Aritra Mitra, Hamed Hassani, George J. Pappas

A Dynamical Systems Perspective on Discrete Optimization

Tong Guanchun, Michael Muehlebach

Offline Model-Based Reinforcement Learning for Tokamak Control

Ian Char, Joseph Abbate, Laszlo Bardoczi, Mark Boyer, Youngseog Chung

Best of Both Worlds in Online Control: Competitive Ratio and Policy Regret

Gautam Goel, Naman Agarwal, Karan Singh, Elad Hazan

Rectified Pessimistic-Optimistic Learning for Stochastic Continuum-armed Bandit with Constraints

Hengquan Guo, Zhu Qi, Xin Liu

Lipschitz constant estimation for 1D convolutional neural networks

Patricia Pauli, Dennis Gramlich, Frank Allgöwer

FedSysID: A Federated Approach to Sample-Efficient System Identification

Han Wang, Leonardo Felipe Toso, James Anderson

Model-Based Reinforcement Learning for Cavity Filter Tuning

Doumitrou Daniil Nimara, Mohammadreza Malek-Mohammadi, Petter Ogren, Jieqiang Wei, Vincent Huang

Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems

Tobias Enders, James Harrison, Marco Pavone, Maximilian Schiffer

Online Estimation of the Koopman Operator Using Fourier Features

Tahiya Salam, Alice Kate Li, M. Ani Hsieh

Reachability Analysis-based Safety-Critical Control using Online Fixed-Time Reinforcement Learning

Nick-Marios Kokolakis, Kyriakos G Vamvoudakis, Wassim Haddad

Policy Evaluation in Distributional LQR

Zifan Wang, Yulong Gao, Siyi Wang, Michael M. Zavlanos, Alessandro Abate

Probabilistic Symmetry for Multi-Agent Dynamics

Sophia Huiwen Sun, Robin Walters, Jinxi Li, Rose Yu

Learning Locomotion Skills from MPC in Sensor Space

Majid Khadiv, Avadesh Meduri, Huaijiang Zhu, Ludovic Righetti, Bernhard Schölkopf

Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning

Daniel Tabas, Ahmed S Zamzam, Baosen Zhang

Time Dependent Inverse Optimal Control using Trigonometric Basis Functions

Rahel Rickenbach, Elena Arcari, Melanie Zeilinger

Predictive safety filter using system level synthesis

Antoine Leeman, Johannes Köhler, Samir Bennani, Melanie Zeilinger

Learning Coherent Clusters in Weakly-Connected Network Systems

Hancheng Min, Enrique Mallada

CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces

Elie Aljalbout, Maximilian Karl, Patrick van der Smagt

Online switching control with stability and regret guarantees

Yingying Li, James A Preiss, Na Li, Yiheng Lin, Adam Wierman

Learning-enhanced Nonlinear Model Predictive Control using Knowledge-based Neural Ordinary Differential Equations and Deep Ensembles

Kong Yao Chee, M. Ani Hsieh, Nikolai Matni

Provably Efficient Model-free RL in Leader-Follower MDP with Linear Function Approximation

Arnob Ghosh

Learning Stability Attention in Vision-based End-to-end Driving Policies

Tsun-Hsuan Wang, Wei Xiao, Makram Chahine, Alexander Amini, Ramin Hasani

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

Alessio Russo

Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control

Lukas Kesper, Sebastian Trimpe, Dominik Baumann

Satellite Navigation and Coordination with Limited Information Sharing

Sydney Dolan, Siddharth Nayak, Hamsa Balakrishnan

Frequency Domain Gaussian Process Models for $H^∞$ Uncertainties

Alex Devonport, Peter Seiler, Murat Arcak

Regret Guarantees for Online Deep Control

Xinyi Chen, Edgar Minasyan, Jason D. Lee, Elad Hazan

Competing Bandits in Time Varying Matching Markets

Deepan Muthirayan, Chinmay Maheshwari, Pramod Khargonekar, Shankar Sastry

On Controller Reduction in Linear Quadratic Gaussian Control with Performance Bounds

Zhaolin Ren, Yang Zheng, Maryam Fazel, Na Li

Detection of Man-in-the-Middle Attacks in Model-Free Reinforcement Learning

Rishi Rani, Massimo Franceschetti

Data-driven Stochastic Output-Feedback Predictive Control: Recursive Feasibility through Interpolated Initial Conditions

Guanru Pan, Ruchuan Ou, Timm Faulwasser

Probabilistic Verification of ReLU Neural Networks via Characteristic Functions

Joshua Pilipovsky, Vignesh Sivaramakrishnan, Meeko Oishi, Panagiotis Tsiotras

Failing with Grace: Learning Neural Network Controllers that are Boundedly Unsafe

Panagiotis Vlantis, Leila Bridgeman, Michael Zavlanos

CT-DQN: Control-Tutored Deep Reinforcement Learning

Francesco De Lellis, Marco Coraggio, Giovanni Russo, Mirco Musolesi, Mario di Bernardo

Physics-Guided Active Learning of Environmental Flow Fields

Reza Khodayi-Mehr, Pingcheng Jian, Michael M. Zavlanos

A Learning and Control Perspective for Microfinance

Xiyu Deng, Christian Kurniawan, Adhiraj Chakraborty, Assane Gueye, Niangjun Chen

Congestion Control of Vehicle Traffic Networks by Learning Structural and Temporal Patterns

Soojean Han, Soon-Jo Chung, Johanna Gustafson

Data-driven memory-dependent abstractions of dynamical systems

Adrien Banse, Licio Romao, Alessandro Abate, Raphael Jungers

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

Distributionally Robust Lyapunov Function Search Under Uncertainty

Kehan Long, Yinzhuang Yi, Jorge Cortes, Nikolay Atanasov

Agile Catching with Whole-Body MPC and Blackbox Policy Learning

Saminda Abeyruwan, Alex Bewley, Nicholas Matthew Boffi, Krzysztof Marcin Choromanski, David B D’Ambrosio

Nonlinear Controllability and Function Representation by Neural Stochastic Differential Equations

Tanya Veeravalli, Maxim Raginsky

Model-based Validation as Probabilistic Inference

Harrison Delecki, Anthony Corso, Mykel Kochenderfer

Top-k data selection via distributed sample quantile inference

Xu Zhang, Marcos M. Vasconcelos

Hierarchical Policy Blending As Optimal Transport

An Thai Le, Kay Hansel, Jan Peters, Georgia Chalvatzaki

Probabilistic Safeguard for Reinforcement Learning Using Safety Index Guided Gaussian Process Models

Weiye Zhao, Tairan He, Changliu Liu

Continuous Versatile Jumping Using Learned Action Residuals

Yuxiang Yang, Xiangyun Meng, Wenhao Yu, Tingnan Zhang, Jie Tan

Learning Object-Centric Dynamic Modes from Video and Emerging Properties

Armand Comas, Christian Fernandez Lopez, Sandesh Ghimire, Haolin Li, Mario Sznaier

Lie Group Forced Variational Integrator Networks for Learning and Control of Robot Systems

Valentin Duruisseaux, Thai P. Duong, Melvin Leok, Nikolay Atanasov

Roll-Drop: accounting for observation noise with a single parameter

Luigi Campanaro, Daniele De Martini, Siddhant Gangapurwala, Wolfgang Merkt, Ioannis Havoutis

CatlNet: Learning Communication and Coordination Policies from CaTL+ Specifications

Wenliang Liu, Kevin Leahy, Zachary Serlin, Calin Belta

Multi-Agent Reinforcement Learning with Reward Delays

Yuyang Zhang, Runyu Zhang, Yuantao Gu, Na Li

Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks

Cyrus Neary, Ufuk Topcu

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

Transportation-Inequalities, Lyapunov Stability and Sampling for Dynamical Systems on Continuous State Space

Muhammad Abdullah Naeem, Miroslav Pajic

Deep Off-Policy Iterative Learning Control

Swaminathan Gurumurthy, J Zico Kolter, Zachary Manchester

Practical Critic Gradient based Actor Critic for On-Policy Reinforcement Learning

Swaminathan Gurumurthy, Zachary Manchester, J Zico Kolter

A finite-sample analysis of multi-step temporal difference estimates

Yaqi Duan, Martin J. Wainwright

Accelerating Trajectory Generation for Quadrotors Using Transformers

Srinath Tankasala, Mitch Pryor

Multi-Task Imitation Learning for Linear Dynamical Systems

Thomas T. Zhang, Katie Kang, Bruce D Lee, Claire Tomlin, Sergey Levine

Automatic Integration for Fast and Interpretable Neural Point Processes

Zihao Zhou, Rose Yu

Adaptive Regret for Control of Time-Varying Dynamics

Paula Gradu, Elad Hazan, Edgar Minasyan

Time-Incremental Learning of Temporal Logic Classifiers Using Decision Trees

Erfan Aasi, Mingyu Cai, Cristian Ioan Vasile, Calin Belta

A Reinforcement Learning Look at Risk-Sensitive Linear Quadratic Gaussian Control

Leilei Cui, Tamer Basar, Zhong-Ping Jiang

Physics-enhanced Gaussian Process Variational Autoencoder

Thomas Beckers, Qirui Wu, George J. Pappas

Template-Based Piecewise Affine Regression

Guillaume O Berger, Sriram Sankaranarayanan

The Impact of the Geometric Properties of the Constraint Set in Safe Optimization with Bandit Feedback

Spencer Hutchinson, Berkay Turan, Mahnoosh Alizadeh

Certified Invertibility in Neural Networks via Mixed-Integer Programming

Tianqi Cui, Thomas Bertalan, George J. Pappas, Manfred Morari, Yannis Kevrekidis

Compositional Learning-based Planning for Vision POMDPs

Sampada Deglurkar, Michael H Lim, Johnathan Tucker, Zachary N Sunberg, Aleksandra Faust

Probabilistic Invariance for Gaussian Process State Space Models

Paul Griffioen, Alex Devonport, Murat Arcak

Can Learning Deteriorate Control? Analyzing Computational Delays in Gaussian Process-Based Event-Triggered Online Learning

Xiaobing Dai, Armin Lederer, Zewen Yang, Sandra Hirche

Targeted Adversarial Attacks against Neural Network Trajectory Predictors

Kaiyuan Tan, Jun Wang, Yiannis Kantaros

Designing System Level Synthesis Controllers for Nonlinear Systems with Stability Guarantees

Lauren E Conger, Sydney Vernon, Eric Mazumdar

Automated Reachability Analysis of Neural Network-Controlled Systems via Adaptive Polytopes

Taha Entesari, Mahyar Fazlyab

Modified Policy Iteration for Exponential Cost Risk Sensitive MDPs

Yashaswini Murthy, Mehrdad Moharrami, R. Srikant

Concentration Phenomenon for Random Dynamical Systems: An Operator Theoretic Approach

Muhammad Abdullah Naeem

Efficient Reinforcement Learning Through Trajectory Generation

Wenqi Cui, Linbin Huang, Weiwei Yang, Baosen Zhang

A Generalizable Physics-informed Learning Framework for Risk Probability Estimation

Zhuoyuan Wang, Yorie Nakahira

Operator Learning for Nonlinear Adaptive Control

Luke Bhan, Yuanyuan Shi, Miroslav Krstic

Equilibria of Fully Decentralized Learning in Networked Systems

Yan Jiang, Wenqi Cui, Baosen Zhang, Jorge Cortes

Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning

Dongsheng Ding, Xiaohan Wei, Zhuoran Yang, Zhaoran Wang, Mihailo Jovanovic

Adaptive Conformal Prediction for Motion Planning among Dynamic Agents

Anushri Dixit, Lars Lindemann, Skylar X Wei, Matthew Cleaveland, George J. Pappas

In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States

Fernando Castaneda, Haruki Nishimura, Rowan Thomas McAllister, Koushil Sreenath, Adrien Gaidon

Compositional Neural Certificates for Networked Dynamical Systems

Songyuan Zhang, Yumeng Xiu, Guannan Qu, Chuchu Fan

Learning Policy-Aware Models for Model-Based Reinforcement Learning via Transition Occupancy Matching

Yecheng Jason Ma, Kausik Sivakumar, Jason Yan, Osbert Bastani, Dinesh Jayaraman

Regret Analysis of Online LQR Control via Trajectory Prediction and Tracking

Yitian Chen, Timothy L Molloy, Tyler Summers, Iman Shames

Full Gradient Deep Reinforcement Learning for Average-Reward Criterion

Tejas Pagare, Vivek Borkar, Konstantin Avrachenkov

End-to-End Learning to Warm-Start for Real-Time Quadratic Optimization

Rajiv Sambharya, Georgina Hall, Brandon Amos, Bartolomeo Stellato

Learning Robust State Observers using Neural ODEs

Keyan Miao, Konstantinos Gatsis

Sample Complexity Bound for Evaluating the Robust Observer’s Performance under Coprime Factors Uncertainty

Serban Sabau, Yifei Zhang, Sourav Kumar Ukil

Policy Gradient Play with Networked Agents in Markov Potential Games

Sarper Aydin, Ceyhun Eksin