L4DC 2022

90 papers

A Piecewise Learning Framework for Control of Unknown Nonlinear Systems with Stability Guarantees Milad Farsi, Yinan Li, Ye Yuan, Jun Liu

A Simple and Efficient Sampling-Based Algorithm for General Reachability Analysis Thomas Lew, Lucas Janson, Riccardo Bonalli, Marco Pavone

Accelerating Dynamical System Simulations with Contracting and Physics-Projected Neural-Newton Solvers Samuel Chevalier, Jochen Stiasny, Spyros Chatzivasileiadis

Accelerating Model-Free Policy Optimization Using Model-Based Gradient: A Composite Optimization Perspective Yansong Li, Shuo Han

Adaptive Model Predictive Control by Learning Classifiers Rel Guzman, Rafael Oliveira, Fabio Ramos

Adaptive Stochastic MPC Under Unknown Noise Distribution Charis Stamouli, Anastasios Tsiamis, Manfred Morari, George J. Pappas

Adaptive Variants of Optimal Feedback Policies Brett Lopez, Jean-Jacques Slotine

Adversarially Regularized Policy Learning Guided by Trajectory Optimization Zhigen Zhao, Simiao Zuo, Tuo Zhao, Ye Zhao

Adversarially Robust Stability Certificates Can Be Sample-Efficient Thomas Zhang, Stephen Tu, Nicholas Boffi, Jean-Jacques Slotine, Nikolai Matni

Automated Design of Grey-Box Recurrent Neural Networks for Fault Diagnosis Using Structural Models and Causal Information Daniel Jung

Barrier Bayesian Linear Regression: Online Learning of Control Barrier Conditions for Safety-Critical Control of Uncertain Systems Lukas Brunke, Siqi Zhou, Angela P. Schoellig

Block Contextual MDPs for Continual Learning Shagun Sodhani, Franziska Meier, Joelle Pineau, Amy Zhang

Bounding the Difference Between Model Predictive Control and Neural Networks Ross Drummond, Stephen Duncan, Mathew Turner, Patricia Pauli, Frank Allgower

Can Foundation Models Perform Zero-Shot Task Specification for Robot Manipulation? Yuchen Cui, Scott Niekum, Abhinav Gupta, Vikash Kumar, Aravind Rajeswaran

Certified Robustness via Locally Biased Randomized Smoothing Brendon G. Anderson, Somayeh Sojoudi

Clustering-Based Mode Reduction for Markov Jump Systems Zhe Du, Necmiye Ozay, Laura Balzano

Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control Francesco De Lellis, Marco Coraggio, Giovanni Russo, Mirco Musolesi, Mario Bernardo

Convergence and Stability of the Stochastic Proximal Point Algorithm with Momentum Junhyung Lyle Kim, Panos Toulis, Anastasios Kyrillidis

Convergence Rates of Two-Time-Scale Gradient Descent-Ascent Dynamics for Solving Nonconvex Min-Max Problems Thinh Doan

Data-Augmented Contact Model for Rigid Body Simulation Yifeng Jiang, Jiazheng Sun, C. Karen Liu

Data-Driven Chance Constrained Control Using Kernel Distribution Embeddings Adam Thorpe, Thomas Lew, Meeko Oishi, Marco Pavone

Data-Driven Control of Unknown Linear Systems via Quantized Feedback Feiran Zhao, Xingchen Li, Keyou You

Data-Driven Controller Synthesis of Unknown Nonlinear Polynomial Systems via Control Barrier Certificates Ameneh Nejati, Bingzhuo Zhong, Marco Caccamo, Majid Zamani

Data-Driven Safety Verification of Stochastic Systems via Barrier Certificates: A Wait-and-Judge Approach Ali Salamati, Majid Zamani

Data-Enabled Gradient Flow as Feedback Controller: Regulation of Linear Dynamical Systems to Minimizers of Unknown Functions Liliaokeawawa Cothren, Gianluca Bianchin, Emiliano Dall’Anese

Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models Jose Luis Vazquez Espinoza, Alexander Liniger, Wilko Schwarting, Daniela Rus, Luc Van Gool

Diffeomorphic Transforms for Generalised Imitation Learning Weiming Zhi, Tin Lai, Lionel Ott, Fabio Ramos

Distributed Control Using Reinforcement Learning with Temporal-Logic-Based Reward Shaping Ningyuan Zhang, Wenliang Liu, Calin Belta

Distributed Neural Network Control with Dependability Guarantees: A Compositional Port-Hamiltonian Approach Luca Furieri, Clara Lucía Galimberti, Muhammad Zakwan, Giancarlo Ferrari-Trecate

Distributed Stochastic Nash Equilibrium Learning in Locally Coupled Network Games with Unknown Parameters Yuanhanqing Huang, Jianghai Hu

Dynamic Learning of Correlation Potentials for a Time-Dependent Kohn-Sham System Harish S. Bhat, Kevin Collins, Prachi Gupta, Christine M. Isborn

Experience Replay with Likelihood-Free Importance Weights Samarth Sinha, Jiaming Song, Animesh Garg, Stefano Ermon

Formal Synthesis of Safety Controllers for Unknown Stochastic Control Systems Using Gaussian Process Learning Rameez Wajid, Asad Ullah Awan, Majid Zamani

Generalization Bounded Implicit Learning of Nearly Discontinuous Functions Bibit Bianchini, Mathew Halm, Nikolai Matni, Michael Posa

Gradient and Projection Free Distributed Online Min-Max Resource Optimization Jingrong Wang, Ben Liang

I-SpaSP: Structured Neural Pruning via Sparse Signal Recovery Cameron R. Wolfe, Anastasios Kyrillidis

Improving Dynamic Regret in Distributed Online Mirror Descent Using Primal and Dual Information Nima Eshraghi, Ben Liang

Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits Alan Yang, Jie Xiong, Maxim Raginsky, Elyse Rosenbaum

Joint Synthesis of Safety Certificate and Safe Control Policy Using Constrained Reinforcement Learning Haitong Ma, Changliu Liu, Shengbo Eben Li, Sifa Zheng, Jianyu Chen

Learning Distributed Channel Access Policies for Networked Estimation: Data-Driven Optimization in the Mean-Field Regime Marcos Vasconcelos

Learning Linear Complementarity Systems Wanxin Jin, Alp Aydinoglu, Mathew Halm, Michael Posa

Learning Linear Models Using Distributed Iterative Hessian Sketching Han Wang, James Anderson

Learning POMDP Models with Similarity Space Regularization: A Linear Gaussian Case Study Yujie Yang, Jianyu Chen, Shengbo Li

Learning Reversible Symplectic Dynamics Riccardo Valperga, Kevin Webster, Dmitry Turaev, Victoria Klein, Jeroen Lamb

Learning Spatio-Temporal Specifications for Dynamical Systems Suhail Alsalehi, Erfan Aasi, Ron Weiss, Calin Belta

Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time Guarantees Siliang Zeng, Tianyi Chen, Alfredo Garcia, Mingyi Hong

Learning to Reach, Swim, Walk and Fly in One Trial: Data-Driven Control with Scarce Data and Side Information Franck Djeumou, Ufuk Topcu

Learning-Based Moving Horizon Estimation Through Differentiable Convex Optimization Layers Simon Muntwiler, Kim P. Wabersich, Melanie N. Zeilinger

Learning-Enabled Robust Control with Noisy Measurements Olle Kjellqvist, Anders Rantzer

Mixtures of Controlled Gaussian Processes for Dynamical Modeling of Deformable Objects Ce Xu Zheng, Adriá Colomé, Luis Sentis, Carme Torras

Modeling Partially Observable Systems Using Graph-Based Memory and Topological Priors Steven Morad, Stephan Liwicki, Ryan Kortvelesy, Roberto Mecca, Amanda Prorok

MyoSuite: A Contact-Rich Simulation Suite for Musculoskeletal Motor Control Vittorio Caggiano, Huawei Wang, Guillaume Durandau, Massimo Sartori, Vikash Kumar

Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks Ryan Sander, Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Sertac Karaman, Daniela Rus

Neural Gaits: Learning Bipedal Locomotion via Control Barrier Functions and Zero Dynamics Policies Ivan Dario Jimenez Rodriguez, Noel Csomay-Shanklin, Yisong Yue, Aaron D. Ames

Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling Franck Djeumou, Cyrus Neary, Eric Goubault, Sylvie Putot, Ufuk Topcu

Neural Point Process for Learning Spatiotemporal Event Dynamics Zihao Zhou, Xingyi Yang, Ryan Rossi, Handong Zhao, Rose Yu

Noise Handling in Data-Driven Predictive Control: A Strategy Based on Dynamic Mode Decomposition Andrea Sassella, Valentina Breschi, Simone Formentin

On the Effectiveness of Iterative Learning Control Anirudh Vemula, Wen Sun, Maxim Likhachev, J. Andrew Bagnell

On the Heterogeneity of Independent Learning Dynamics in Zero-Sum Stochastic Games Muhammed Sayin, Kemal Cetiner

On the Sample Complexity of Stability Constrained Imitation Learning Stephen Tu, Alexander Robey, Tingnan Zhang, Nikolai Matni

Online Estimation and Control with Optimal Pathlength Regret Gautam Goel, Babak Hassibi

Online No-Regret Model-Based Meta RL for Personalized Navigation Yuda Song, Yuan Ye, Wen Sun, Kris Kitani

OpReg-Boost: Learning to Accelerate Online Algorithms with Operator Regression Nicola Bastianello, Andrea Simonetto, Emiliano Dall’Anese

Optimal Control with Learning on the Fly: System with Unknown Drift Daniel Gurevich, Debdipta Goswami, Charles L. Fefferman, Clarence W. Rowley

Optimal Pointing Sequences in Spacecraft Formation Flying Using Online Planning with Resource Constraints Samuel Low, Mykel Kochenderfer

PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems Ting-Han Fan, Xian Yeow Lee, Yubo Wang

PRISM: Recurrent Neural Networks and Presolve Methods for Fast Mixed-Integer Optimal Control Abhishek Cauligi, Ankush Chakrabarty, Stefano Di Cairano, Rien Quirynen

Reinforcement Learning with Almost Sure Constraints Agustin Castellano, Hancheng Min, Enrique Mallada, Juan Andrés Bazerque

Resiliency of Perception-Based Controllers Against Attacks Amir Khazraei, Henry Pfister, Miroslav Pajic

Robust Data-Driven Output Feedback Control via Bootstrapped Multiplicative Noise Benjamin Gravell, Iman Shames, Tyler Summers

Robust Graph Neural Networks via Probabilistic Lipschitz Constraints Raghu Arghal, Eric Lei, Shirin Saeedi Bidokhti

Robust Online Control with Model Misspecification Udaya Ghai, Xinyi Chen, Elad Hazan, Alexandre Megretski

Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach Saber Jafarpour, Matthew Abate, Alexander Davydov, Francesco Bullo, Samuel Coogan

Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics Zhichao Li, Thai Duong, Nikolay Atanasov

Safe Control with Minimal Regret Andrea Martin, Luca Furieri, Florian Dörfler, John Lygeros, Giancarlo Ferrari-Trecate

Safe Control with Neural Network Dynamic Models Tianhao Wei, Changliu Liu

Safe Reinforcement Learning with Chance-Constrained Model Predictive Control Samuel Pfrommer, Tanmay Gautam, Alec Zhou, Somayeh Sojoudi

Safety-Aware Preference-Based Learning for Safety-Critical Control Ryan Cosner, Maegan Tucker, Andrew Taylor, Kejun Li, Tamas Molnar, Wyatt Ubelacker, Anil Alan, Gabor Orosz, Yisong Yue, Aaron Ames

Sample Complexity of the Robust LQG Regulator with Coprime Factors Uncertainty Yifei Zhang, Sourav Ukil, Ephraim Neimand, Serban Sabau, Myron Hohil

Sample-Based Distributional Policy Gradient Rahul Singh, Keuntaek Lee, Yongxin Chen

Sliding-Seeking Control: Model-Free Optimization with Safety Constraints Felipe Galarza-Jiménez, Jorge Poveda, Emiliano Dall’Anese

Structure-Preserving Learning Using Gaussian Processes and Variational Integrators Jan Brüdigam, Martin Schuck, Alexandre Capone, Stefan Sosnowski, Sandra Hirche

Symplectic Momentum Neural Networks - Using Discrete Variational Mechanics as a Prior in Deep Learning Saul Santos, Monica Ekal, Rodrigo Ventura

Time Varying Regression with Hidden Linear Dynamics Horia Mania, Ali Jadbabaie, Devavrat Shah, Suvrit Sra

Total Energy Shaping with Neural Interconnection and Damping Assignment - Passivity Based Control Santiago Sanchez-Escalonilla Plaza, Rodolfo Reyes-Baez, Bayu Jayawardhana

Tracking and Planning with Spatial World Models Baris Kayalibay, Atanas Mirchev, Patrick Smagt, Justin Bayer

Training Lipschitz Continuous Operators Using Reproducing Kernels Henk van Waarde, Rodolphe Sepulchre

Traversing Time with Multi-Resolution Gaussian Process State-Space Models Krista Longi, Jakob Lindinger, Olaf Duennbier, Melih Kandemir, Arto Klami, Barbara Rakitsch

ValueNetQP: Learned One-Step Optimal Control for Legged Locomotion Julian Viereck, Avadesh Meduri, Ludovic Righetti

Vision-Based System Identification and 3D Keypoint Discovery Using Dynamics Constraints Miguel Jaques, Martin Asenov, Michael Burke, Timothy Hospedales