Papers
546 papers found
Analysis of the Optimization Landscape of Linear Quadratic Gaussian (LQG) Control
Yujie Tang, Yang Zheng, Na Li
A New Objective for Identification of Partially Observed Linear Time-Invariant Dynamical Systems from Input-Output Data
Nicholas Galioto, Alex Arkady Gorodetsky
Approximate Distributionally Robust Nonlinear Optimization with Application to Model Predictive Control: A Functional Approach
Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu
Approximate Midpoint Policy Iteration for Linear Quadratic Control
Benjamin Gravell, Iman Shames, Tyler Summers
ARDL - A Library for Adaptive Robotic Dynamics Learning
Joshua Smith, Michael Mistry
A unified framework for Hamiltonian deep neural networks
Clara Lucía Galimberti, Liang Xu, Giancarlo Ferrari Trecate
Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution
Elliott Skomski, Ján Drgoňa, Aaron Tuor
Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data
Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems
Rui Wang, Danielle Maddix, Christos Faloutsos et al.
Cautious Bayesian Optimization for Efficient and Scalable Policy Search
Lukas P. Fröhlich, Melanie N. Zeilinger, Edgar D. Klenske
Certainty Equivalent Perception-Based Control
Sarah Dean, Benjamin Recht
Certifying Incremental Quadratic Constraints for Neural Networks via Convex Optimization
Navid Hashemi, Justin Ruths, Mahyar Fazlyab
Chance-constrained quasi-convex optimization with application to data-driven switched systems control
Guillaume O. Berger, Raphaël M. Jungers, Zheming Wang
Contraction L1-Adaptive Control using Gaussian Processes
Aditya Gahlawat, Arun Lakshmanan, Lin Song et al.
Control of Unknown (Linear) Systems with Receding Horizon Learning
Christian Ebenbauer, Fabian Pfitz, Shuyou Yu
Data-Driven Abstraction of Monotone Systems
Anas Makdesi, Antoine Girard, Laurent Fribourg
Data-Driven Controller Design via Finite-Horizon Dissipativity
Nils Wieler, Julian Berberich, Anne Koch et al.
Data-driven design of switching reference governors for brake-by-wire applications
Andrea Sassella, Valentina Breschi, Simone Formentin
Data-Driven Reachability Analysis Using Matrix Zonotopes
Amr Alanwar, Anne Koch, Frank Allgöwer et al.
Data-Driven System Level Synthesis
Anton Xue, Nikolai Matni
Decoupling dynamics and sampling: RNNs for unevenly sampled data and flexible online predictions
Signe Moe, Camilla Sterud
Domain Adaptation Using System Invariant Dynamics Models
Sean J. Wang, Aaron M. Johnson
Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety
Noel Csomay-Shanklin, Ryan K. Cosner, Min Dai et al.
Estimating Disentangled Belief about Hidden State and Hidden Task for Meta-Reinforcement Learning
Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo
Exploiting Sparsity for Neural Network Verification
Matthew Newton, Antonis Papachristodoulou