L4DC 2020

99 papers

A Duality Approach for Regret Minimization in Average-Award Ergodic Markov Decision Processes Hao Gong, Mengdi Wang

A Finite-Sample Deviation Bound for Stable Autoregressive Processes Rodrigo A. González, Cristian R. Rojas

A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via Differentiable Physics Engines Kun Wang, Mridul Aanjaneya, Kostas Bekris

A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control Jia-Jie Zhu, Bernhard Schoelkopf, Moritz Diehl

A Spatially and Temporally Attentive Joint Trajectory Prediction Framework for Modeling Vessel Intent Jasmine Sekhon, Cody Fleming

A Theoretical Analysis of Deep Q-Learning Jianqing Fan, Zhaoran Wang, Yuchen Xie, Zhuoran Yang

Actively Learning Gaussian Process Dynamics Mona Buisson-Fenet, Friedrich Solowjow, Sebastian Trimpe

Bayesian Joint State and Parameter Tracking in Autoregressive Models Ismail Senoz, Albert Podusenko, Wouter M. Kouw, Bert Vries

Bayesian Learning with Adaptive Load Allocation Strategies Manxi Wu, Saurabh Amin, Asuman Ozdaglar

Bayesian Model Predictive Control: Efficient Model Exploration and Regret Bounds Using Posterior Sampling Kim Peter Wabersich, Melanie Zeilinger

Black-Box Continuous-Time Transfer Function Estimation with Stability Guarantees: A Kernel-Based Approach Mirko Mazzoleni, Matteo Scandella, Simone Formentin, Fabio Previdi

Constrained Upper Confidence Reinforcement Learning Liyuan Zheng, Lillian Ratliff

Constraint Management for Batch Processes Using Iterative Learning Control and Reference Governors Aidan Laracy, Hamid Ossareh

Contracting Implicit Recurrent Neural Networks: Stable Models with Improved Trainability Max Revay, Ian Manchester

Counterfactual Programming for Optimal Control Luiz F. O. Chamon, Santiago Paternain, Alejandro Ribeiro

Data-Driven Distributed Predictive Control via Network Optimization Ahmed Allibhoy, Jorge Cortes

Data-Driven Distributionally Robust LQR with Multiplicative Noise Peter Coppens, Mathijs Schuurmans, Panagiotis Patrinos

Data-Driven Identification of Approximate Passive Linear Models for Nonlinear Systems S. Sivaranjani, Etika Agarwal, Vijay Gupta

Direct Data-Driven Control with Embedded Anti-Windup Compensation Valentina Breschi, Simone Formentin

Distributed Reinforcement Learning for Decentralized Linear Quadratic Control: A Derivative-Free Policy Optimization Approach Yingying Li, Yujie Tang, Runyu Zhang, Na Li

Dual Stochastic MPC for Systems with Parametric and Structural Uncertainty Elena Arcari, Lukas Hewing, Max Schlichting, Melanie Zeilinger

Efficient Large-Scale Gaussian Process Bandits by Believing Only Informative Actions Amrit Singh Bedi, Dheeraj Peddireddy, Vaneet Aggarwal, Alec Koppel

Encoding Physical Constraints in Differentiable Newton-Euler Algorithm Giovanni Sutanto, Austin Wang, Yixin Lin, Mustafa Mukadam, Gaurav Sukhatme, Akshara Rai, Franziska Meier

Estimating Reachable Sets with Scenario Optimization Alex Devonport, Murat Arcak

Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems Muhammad Asif Rana, Anqi Li, Dieter Fox, Byron Boots, Fabio Ramos, Nathan Ratliff

Exploiting Model Sparsity in Adaptive MPC: A Compressed Sensing Viewpoint Monimoy Bujarbaruah, Charlott Vallon

Faster Saddle-Point Optimization for Solving Large-Scale Markov Decision Processes Joan Bas Serrano, Gergely Neu

Feed-Forward Neural Networks with Trainable Delay Xunbi A. Ji, Tamás G. Molnár, Sergei S. Avedisov, Gábor Orosz

Feynman-Kac Neural Network Architectures for Stochastic Control Using Second-Order FBSDE Theory Marcus Pereira, Ziyi Wang, Tianrong Chen, Emily Reed, Evangelos Theodorou

Finite Sample System Identification: Optimal Rates and the Role of Regularization Yue Sun, Samet Oymak, Maryam Fazel

Finite-Time Performance of Distributed Two-Time-Scale Stochastic Approximation Thinh Doan, Justin Romberg

Fitting a Linear Control Policy to Demonstrations with a Kalman Constraint Malayandi Palan, Shane Barratt, Alex McCauley, Dorsa Sadigh, Vikas Sindhwani, Stephen Boyd

Generating Robust Supervision for Learning-Based Visual Navigation Using Hamilton-Jacobi Reachability Anjian Li, Somil Bansal, Georgios Giovanis, Varun Tolani, Claire Tomlin, Mo Chen

Hamilton-Jacobi-Bellman Equations for Q-Learning in Continuous Time Jeongho Kim, Insoon Yang

Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation Hany Abdulsamad, Jan Peters

Identifying Mechanical Models of Unknown Objects with Differentiable Physics Simulations Changkyu Song, Abdeslam Boularias

Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning Fernando Castañeda, Mathias Wulfman, Ayush Agrawal, Tyler Westenbroek, Shankar Sastry, Claire Tomlin, Koushil Sreenath

Improving Robustness via Risk Averse Distributional Reinforcement Learning Rahul Singh, Qinsheng Zhang, Yongxin Chen

Information Theoretic Model Predictive Q-Learning Mohak Bhardwaj, Ankur Handa, Dieter Fox, Byron Boots

Keyframing the Future: Keyframe Discovery for Visual Prediction and Planning Karl Pertsch, Oleh Rybkin, Jingyun Yang, Shenghao Zhou, Konstantinos Derpanis, Kostas Daniilidis, Joseph Lim, Andrew Jaegle

L1-GP: L1 Adaptive Control with Bayesian Learning Aditya Gahlawat, Pan Zhao, Andrew Patterson, Naira Hovakimyan, Evangelos Theodorou

Lambda-Policy Iteration with Randomization for Contractive Models with Infinite Policies: Well-Posedness and Convergence Yuchao Li, Karl Henrik Johansson, Jonas Mårtensson

Learning Constrained Dynamics with Gauss’ Principle Adhering Gaussian Processes Andreas Geist, Sebastian Trimpe

Learning Convex Optimization Control Policies Akshay Agrawal, Shane Barratt, Stephen Boyd, Bartolomeo Stellato

Learning Dynamical Systems with Side Information Amir Ali Ahmadi, Bachir El Khadir

Learning for Safety-Critical Control with Control Barrier Functions Andrew Taylor, Andrew Singletary, Yisong Yue, Aaron Ames

Learning Navigation Costs from Demonstrations with Semantic Observations Tianyu Wang, Vikas Dhiman, Nikolay Atanasov

Learning Nonlinear Dynamical Systems from a Single Trajectory Dylan Foster, Tuhin Sarkar, Alexander Rakhlin

Learning Solutions to Hybrid Control Problems Using Benders Cuts Sandeep Menta, Joseph Warrington, John Lygeros, Manfred Morari

Learning Supported Model Predictive Control for Tracking of Periodic References Janine Matschek, Rolf Findeisen

Learning the Globally Optimal Distributed LQ Regulator Luca Furieri, Yang Zheng, Maryam Kamgarpour

Learning the Model-Free Linear Quadratic Regulator via Random Search Hesameddin Mohammadi, Mihailo R. Jovanovic’, Mahdi Soltanolkotabi

Learning to Correspond Dynamical Systems Nam Hee Kim, Zhaoming Xie, Michiel Panne

Learning to Plan via Deep Optimistic Value Exploration Tim Seyde, Wilko Schwarting, Sertac Karaman, Daniela Rus

Learning-Based Stochastic Model Predictive Control with State-Dependent Uncertainty Angelo Domenico Bonzanini, Ali Mesbah

Linear Antisymmetric Recurrent Neural Networks Signe Moe, Filippo Remonato, Esten Ingar Grøtli, Jan Tommy Gravdahl

Localized Active Learning of Gaussian Process State Space Models Alexandre Capone, Gerrit Noske, Jonas Umlauft, Thomas Beckers, Armin Lederer, Sandra Hirche

Localized Learning of Robust Controllers for Networked Systems with Dynamic Topology Soojean Han

LSTM Neural Networks: Input to State Stability and Probabilistic Safety Verification Fabio Bonassi, Enrico Terzi, Marcello Farina, Riccardo Scattolini

Lyceum: An Efficient and Scalable Ecosystem for Robot Learning Colin Summers, Kendall Lowrey, Aravind Rajeswaran, Siddhartha Srinivasa, Emanuel Todorov

Model-Based Reinforcement Learning with Value-Targeted Regression Zeyu Jia, Lin Yang, Csaba Szepesvari, Mengdi Wang

Model-Predictive Control via Cross-Entropy and Gradient-Based Optimization Homanga Bharadhwaj, Kevin Xie, Florian Shkurti

NeuralExplorer: State Space Exploration of Closed Loop Control Systems Using Neural Networks Manish Goyal, Parasara Sridhar Duggirala

NeurOpt: Neural Network Based Optimization for Building Energy Management and Climate Control Achin Jain, Francesco Smarra, Enrico Reticcioli, Alessandro D’Innocenzo, Manfred Morari

Objective Mismatch in Model-Based Reinforcement Learning Nathan Lambert, Brandon Amos, Omry Yadan, Roberto Calandra

On Simulation and Trajectory Prediction with Gaussian Process Dynamics Lukas Hewing, Elena Arcari, Lukas P. Fröhlich, Melanie N. Zeilinger

On the Robustness of Data-Driven Controllers for Linear Systems Rajasekhar Anguluri, Abed Alrahman Al Makdah, Vaibhav Katewa, Fabio Pasqualetti

Online Data Poisoning Attacks Xuezhou Zhang, Xiaojin Zhu, Laurent Lessard

Optimistic Robust Linear Quadratic Dual Control Jack Umenberger, Thomas B. Schön

Parameter Optimization for Learning-Based Control of Control-Affine Systems Armin Lederer, Alexandre Capone, Sandra Hirche

Periodic Q-Learning Donghwan Lee, Niao He

Plan2Vec: Unsupervised Representation Learning by Latent Plans Ge Yang, Amy Zhang, Ari Morcos, Joelle Pineau, Pieter Abbeel, Roberto Calandra

Planning from Images with Deep Latent Gaussian Process Dynamics Nathanael Bosch, Jan Achterhold, Laura Leal-Taixé, Jörg Stückler

Policy Learning of MDPs with Mixed Continuous/Discrete Variables: A Case Study on Model-Free Control of Markovian Jump Systems Joao Paulo Jansch-Porto, Bin Hu, Geir Dullerud

Policy Optimization for $\mathcal{H}_2$ Linear Control with $\mathcal{H}_\infty$ Robustness Guarantee: Implicit Regularization and Global Convergence Kaiqing Zhang, Bin Hu, Tamer Basar

Practical Reinforcement Learning for MPC: Learning from Sparse Objectives in Under an Hour on a Real Robot Napat Karnchanachari, Miguel Iglesia Valls, David Hoeller, Marco Hutter

Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics Mohammad Javad Khojasteh, Vikas Dhiman, Massimo Franceschetti, Nikolay Atanasov

Regret Bound for Safe Gaussian Process Bandit Optimization Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis

Riccati Updates for Online Linear Quadratic Control Mohammad Akbari, Bahman Gharesifard, Tamas Linder

Robust Deep Learning as Optimal Control: Insights and Convergence Guarantees Jacob H. Seidman, Mahyar Fazlyab, Victor M. Preciado, George J. Pappas

Robust Guarantees for Perception-Based Control Sarah Dean, Nikolai Matni, Benjamin Recht, Vickie Ye

Robust Learning-Based Control via Bootstrapped Multiplicative Noise Benjamin Gravell, Tyler Summers

Robust Online Model Adaptation by Extended Kalman Filter with Exponential Moving Average and Dynamic Multi-Epoch Strategy Abulikemu Abuduweili, Changliu Liu

Robust Regression for Safe Exploration in Control Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, Yisong Yue

Safe Non-Smooth Black-Box Optimization with Application to Policy Search Ilnura Usmanova, Andreas Krause, Maryam Kamgarpour

Sample Complexity of Kalman Filtering for Unknown Systems Anastasios Tsiamis, Nikolai Matni, George Pappas

Scalable Reinforcement Learning of Localized Policies for Multi-Agent Networked Systems Guannan Qu, Adam Wierman, Na Li

Smart Forgetting for Safe Online Learning with Gaussian Processes Jonas Umlauft, Thomas Beckers, Alexandre Capone, Armin Lederer, Sandra Hirche

Sparse and Low-Bias Estimation of High Dimensional Vector Autoregressive Models Trevor Ruiz, Sharmodeep Bhattacharyya, Mahesh Balasubramanian, Kristofer Bouchard

Stable Reinforcement Learning with Unbounded State Space Devavrat Shah, Qiaomin Xie, Zhi Xu

Structured Mechanical Models for Robot Learning and Control Jayesh K. Gupta, Kunal Menda, Zachary Manchester, Mykel Kochenderfer

Structured Variational Inference in Partially Observable Unstable Gaussian Process State Space Models Sebastian Curi, Silvan Melchior, Felix Berkenkamp, Andreas Krause

Tools for Data-Driven Modeling of Within-Hand Manipulation with Underactuated Adaptive Hands Avishai Sintov, Andrew Kimmel, Bowen Wen, Abdeslam Boularias, Kostas Bekris

Toward Fusion Plasma Scenario Planning for NSTX-U Using Machine-Learning-Accelerated Models Mark Boyer

Tractable Reinforcement Learning of Signal Temporal Logic Objectives Harish Venkataraman, Derya Aksaray, Peter Seiler

Uncertain Multi-Agent MILPs: A Data-Driven Decentralized Solution with Probabilistic Feasibility Guarantees Alessandro Falsone, Federico Molinari, Maria Prandini

Universal Simulation of Stable Dynamical Systems by Recurrent Neural Nets Joshua Hanson, Maxim Raginsky

VarNet: Variational Neural Networks for the Solution of Partial Differential Equations Reza Khodayi-Mehr, Michael Zavlanos

Virtual Reference Feedback Tuning with Data-Driven Reference Model Selection Valentina Breschi, Simone Formentin