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2020 – today
- 2024
[j12]Shaoru Chen, Victor M. Preciado, Manfred Morari, Nikolai Matni:
Robust model predictive control with polytopic model uncertainty through System Level Synthesis. Autom. 162: 111431 (2024)- [c85]Thomas T. C. K. Zhang, Leonardo Felipe Toso, James Anderson, Nikolai Matni:
Sample-Efficient Linear Representation Learning from Non-IID Non-Isotropic Data. ICLR 2024 - [c84]Thomas T. C. K. Zhang, Bruce D. Lee, Ingvar M. Ziemann, George J. Pappas, Nikolai Matni:
Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples. ICML 2024 - [c83]Ingvar M. Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni:
Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss. ICML 2024 - [c82]Farhad Nawaz, Tianyu Li, Nikolai Matni, Nadia Figueroa:
Learning Complex Motion Plans using Neural ODEs with Safety and Stability Guarantees. ICRA 2024: 17216-17222 - [c81]Bruce D. Lee, Anders Rantzer, Nikolai Matni:
Nonasymptotic regret analysis of adaptive linear quadratic control with model misspecification. L4DC 2024: 980-992 - [c80]Anish Bhattacharya, Ratnesh Madaan, Fernando Cladera Ojeda, Sai Vemprala, Rogerio Bonatti, Kostas Daniilidis, Ashish Kapoor, Vijay Kumar, Nikolai Matni, Jayesh K. Gupta:
EvDNeRF: Reconstructing Event Data with Dynamic Neural Radiance Fields. WACV 2024: 5834-5843 - [i83]Bruce D. Lee, Anders Rantzer, Nikolai Matni:
Nonasymptotic Regret Analysis of Adaptive Linear Quadratic Control with Model Misspecification. CoRR abs/2401.00073 (2024) - [i82]Hanli Zhang, Anusha Srikanthan, Spencer Folk, Vijay Kumar, Nikolai Matni:
Why Change Your Controller When You Can Change Your Planner: Drag-Aware Trajectory Generation for Quadrotor Systems. CoRR abs/2401.04960 (2024) - [i81]Nikolai Matni, Aaron D. Ames, John C. Doyle:
Towards a Theory of Control Architecture: A quantitative framework for layered multi-rate control. CoRR abs/2401.15185 (2024) - [i80]Ingvar M. Ziemann, Stephen Tu, George J. Pappas
, Nikolai Matni:
Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss. CoRR abs/2402.05928 (2024) - [i79]Bo Wu, Bruce D. Lee, Kostas Daniilidis, Bernadette Bucher, Nikolai Matni:
Uncertainty-Aware Deployment of Pre-trained Language-Conditioned Imitation Learning Policies. CoRR abs/2403.18222 (2024) - [i78]Bruce D. Lee, Ingvar M. Ziemann, George J. Pappas, Nikolai Matni:
Active Learning for Control-Oriented Identification of Nonlinear Systems. CoRR abs/2404.09030 (2024) - [i77]Farhad Nawaz, Shaoting Peng, Lars Lindemann, Nadia Figueroa, Nikolai Matni:
Reactive Temporal Logic-based Planning and Control for Interactive Robotic Tasks. CoRR abs/2404.19594 (2024) - [i76]Anish Bhattacharya, Nishanth Rao, Dhruv Parikh, Pratik Kunapuli, Nikolai Matni, Vijay Kumar:
Vision Transformers for End-to-End Vision-Based Quadrotor Obstacle Avoidance. CoRR abs/2405.10391 (2024) - [i75]Brian Lee, Nikolai Matni:
Single Trajectory Conformal Prediction. CoRR abs/2406.01570 (2024) - [i74]Bruce D. Lee, Leonardo F. Toso, Thomas T. C. K. Zhang, James Anderson, Nikolai Matni:
Regret Analysis of Multi-task Representation Learning for Linear-Quadratic Adaptive Control. CoRR abs/2407.05781 (2024) - [i73]Fengjun Yang, Nikolai Matni:
Coordinating Planning and Tracking in Layered Control Policies via Actor-Critic Learning. CoRR abs/2408.01639 (2024) - [i72]Anusha Srikanthan, Aren Karapetyan, Vijay Kumar, Nikolai Matni:
Closed-loop Analysis of ADMM-based Suboptimal Linear Model Predictive Control. CoRR abs/2409.11351 (2024) - [i71]Ingvar M. Ziemann, Nikolai Matni, George J. Pappas:
State space models, emergence, and ergodicity: How many parameters are needed for stable predictions? CoRR abs/2409.13421 (2024) - [i70]Thomas T. Zhang, Bruce D. Lee, Ingvar M. Ziemann, George J. Pappas, Nikolai Matni:
Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples. CoRR abs/2410.11227 (2024) - 2023
- [j11]Carmen Amo Alonso, Jing Shuang Li, James Anderson, Nikolai Matni:
Distributed and Localized Model-Predictive Control-Part I: Synthesis and Implementation. IEEE Trans. Control. Netw. Syst. 10(2): 1058-1068 (2023) - [j10]Carmen Amo Alonso, Jing Shuang Li, Nikolai Matni, James Anderson:
Distributed and Localized Model Predictive Control - Part II: Theoretical Guarantees. IEEE Trans. Control. Netw. Syst. 10(3): 1113-1123 (2023) - [j9]Lars Lindemann, Lejun Jiang, Nikolai Matni, George J. Pappas:
Risk of Stochastic Systems for Temporal Logic Specifications. ACM Trans. Embed. Comput. Syst. 22(3): 54:1-54:31 (2023) - [c79]Thomas T. C. K. Zhang, Bruce D. Lee, Hamed Hassani, Nikolai Matni:
Adversarial Tradeoffs in Robust State Estimation. ACC 2023: 4083-4089 - [c78]Bruce D. Lee, Ingvar M. Ziemann, Anastasios Tsiamis, Henrik Sandberg, Nikolai Matni:
The Fundamental Limitations of Learning Linear-Quadratic Regulators. CDC 2023: 4053-4060 - [c77]Shaoru Chen, Kong Yao Chee, Nikolai Matni, M. Ani Hsieh, George J. Pappas:
Safety Filter Design for Neural Network Systems via Convex Optimization. CDC 2023: 6356-6363 - [c76]Ingvar M. Ziemann, Anastasios Tsiamis, Bruce D. Lee, Yassir Jedra, Nikolai Matni, George J. Pappas:
A Tutorial on the Non-Asymptotic Theory of System Identification. CDC 2023: 8921-8939 - [c75]Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, Stephen Tu:
The Power of Learned Locally Linear Models for Nonlinear Policy Optimization. ICML 2023: 27737-27821 - [c74]David Brandfonbrener, Stephen Tu, Avi Singh, Stefan Welker, Chad Boodoo, Nikolai Matni, Jake Varley:
Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning. ICRA 2023: 11336-11342 - [c73]Anusha Srikanthan, Fengjun Yang, Igor Spasojevic, Dinesh Thakur, Vijay Kumar, Nikolai Matni:
A Data-Driven Approach to Synthesizing Dynamics-Aware Trajectories for Underactuated Robotic Systems. IROS 2023: 8215-8222 - [c72]Thomas T. C. K. Zhang, Katie Kang, Bruce D. Lee, Claire J. Tomlin, Sergey Levine, Stephen Tu, Nikolai Matni:
Multi-Task Imitation Learning for Linear Dynamical Systems. L4DC 2023: 586-599 - [c71]Kong Yao Chee, M. Ani Hsieh, Nikolai Matni:
Learning-enhanced Nonlinear Model Predictive Control using Knowledge-based Neural Ordinary Differential Equations and Deep Ensembles. L4DC 2023: 1125-1137 - [c70]Ingvar M. Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni:
The noise level in linear regression with dependent data. NeurIPS 2023 - [c69]Haoze Wu, Teruhiro Tagomori, Alexander Robey, Fengjun Yang, Nikolai Matni, George J. Pappas, Hamed Hassani, Corina S. Pasareanu, Clark W. Barrett:
Toward Certified Robustness Against Real-World Distribution Shifts. SaTML 2023: 537-553 - [e1]Nikolai Matni, Manfred Morari, George J. Pappas:
Learning for Dynamics and Control Conference, L4DC 2023, 15-16 June 2023, Philadelphia, PA, USA. Proceedings of Machine Learning Research 211, PMLR 2023 [contents] - [i69]Bruce D. Lee, Ingvar M. Ziemann, Anastasios Tsiamis, Henrik Sandberg, Nikolai Matni:
The Fundamental Limitations of Learning Linear-Quadratic Regulators. CoRR abs/2303.15637 (2023) - [i68]Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, Stephen Tu:
The Power of Learned Locally Linear Models for Nonlinear Policy Optimization. CoRR abs/2305.09619 (2023) - [i67]Ingvar M. Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni:
The noise level in linear regression with dependent data. CoRR abs/2305.11165 (2023) - [i66]Bruce D. Lee, Thomas T. C. K. Zhang, Hamed Hassani, Nikolai Matni:
Performance-Robustness Tradeoffs in Adversarially Robust Control and Estimation. CoRR abs/2305.16415 (2023) - [i65]Anusha Srikanthan, Fengjun Yang, Igor Spasojevic, Dinesh Thakur, Vijay Kumar, Nikolai Matni:
A Data-Driven Approach to Synthesizing Dynamics-Aware Trajectories for Underactuated Robotic Systems. CoRR abs/2307.13782 (2023) - [i64]Farhad Nawaz, Tianyu Li, Nikolai Matni, Nadia Figueroa:
Learning Safe and Stable Motion Plans with Neural Ordinary Differential Equations. CoRR abs/2308.00186 (2023) - [i63]Thomas T. C. K. Zhang, Leonardo Felipe Toso, James Anderson, Nikolai Matni:
Meta-Learning Operators to Optimality from Multi-Task Non-IID Data. CoRR abs/2308.04428 (2023) - [i62]Shaoru Chen, Kong Yao Chee, Nikolai Matni, M. Ani Hsieh, George J. Pappas:
Safety Filter Design for Neural Network Systems via Convex Optimization. CoRR abs/2308.08086 (2023) - [i61]Ingvar M. Ziemann, Anastasios Tsiamis, Bruce D. Lee, Yassir Jedra, Nikolai Matni, George J. Pappas:
A Tutorial on the Non-Asymptotic Theory of System Identification. CoRR abs/2309.03873 (2023) - [i60]Anish Bhattacharya, Ratnesh Madaan, Fernando Cladera Ojeda, Sai Vemprala, Rogerio Bonatti, Kostas Daniilidis, Ashish Kapoor, Vijay Kumar, Nikolai Matni, Jayesh K. Gupta:
EvDNeRF: Reconstructing Event Data with Dynamic Neural Radiance Fields. CoRR abs/2310.02437 (2023) - [i59]Anusha Srikanthan, Vijay Kumar, Nikolai Matni:
Augmented Lagrangian Methods as Layered Control Architectures. CoRR abs/2311.06404 (2023) - 2022
- [c68]Bruce D. Lee, Thomas T. C. K. Zhang, Hamed Hassani, Nikolai Matni:
Performance-Robustness Tradeoffs in Adversarially Robust Linear-Quadratic Control. CDC 2022: 3416-3423 - [c67]Fengjun Yang, Fernando Gama, Somayeh Sojoudi, Nikolai Matni:
Distributed Optimal Control of Graph Symmetric Systems via Graph Filters. CDC 2022: 5245-5252 - [c66]Ingvar M. Ziemann, Anastasios Tsiamis, Henrik Sandberg, Nikolai Matni:
How are policy gradient methods affected by the limits of control? CDC 2022: 5992-5999 - [c65]Shaoru Chen, Ning-Yuan Li, Victor M. Preciado, Nikolai Matni:
Robust Model Predictive Control of Time-Delay Systems through System Level Synthesis. CDC 2022: 6902-6909 - [c64]Ingvar M. Ziemann, Henrik Sandberg, Nikolai Matni:
Single Trajectory Nonparametric Learning of Nonlinear Dynamics. COLT 2022: 3333-3364 - [c63]Anastasios Tsiamis, Ingvar M. Ziemann, Manfred Morari, Nikolai Matni, George J. Pappas:
Learning to Control Linear Systems can be Hard. COLT 2022: 3820-3857 - [c62]Georgios Georgakis, Bernadette Bucher, Anton Arapin, Karl Schmeckpeper, Nikolai Matni, Kostas Daniilidis:
Uncertainty-driven Planner for Exploration and Navigation. ICRA 2022: 11295-11302 - [c61]Stephen Tu, Alexander Robey, Tingnan Zhang, Nikolai Matni:
On the Sample Complexity of Stability Constrained Imitation Learning. L4DC 2022: 180-191 - [c60]Thomas T. C. K. Zhang, Stephen Tu, Nicholas M. Boffi, Jean-Jacques E. Slotine, Nikolai Matni:
Adversarially Robust Stability Certificates can be Sample-Efficient. L4DC 2022: 532-545 - [c59]Bibit Bianchini, Mathew Halm, Nikolai Matni, Michael Posa:
Generalization Bounded Implicit Learning of Nearly Discontinuous Functions. L4DC 2022: 1112-1124 - [c58]Daniel Pfrommer, Thomas T. C. K. Zhang, Stephen Tu, Nikolai Matni:
TaSIL: Taylor Series Imitation Learning. NeurIPS 2022 - [i58]Daniel Pfrommer, Nikolai Matni:
Linear Variational State Space Filtering. CoRR abs/2201.01353 (2022) - [i57]Ingvar M. Ziemann, Henrik Sandberg, Nikolai Matni:
Single Trajectory Nonparametric Learning of Nonlinear Dynamics. CoRR abs/2202.08311 (2022) - [i56]Georgios Georgakis, Bernadette Bucher, Anton Arapin, Karl Schmeckpeper, Nikolai Matni, Kostas Daniilidis:
Uncertainty-driven Planner for Exploration and Navigation. CoRR abs/2202.11907 (2022) - [i55]Carmen Amo Alonso, Jing Shuang Li, Nikolai Matni, James Anderson:
Distributed and Localized Model Predictive Control. Part II: Theoretical Guarantees. CoRR abs/2203.00780 (2022) - [i54]Bruce D. Lee, Thomas T. C. K. Zhang, Hamed Hassani, Nikolai Matni:
Performance-Robustness Tradeoffs in Adversarially Robust Linear-Quadratic Control. CoRR abs/2203.10763 (2022) - [i53]Shaoru Chen, Victor M. Preciado, Manfred Morari, Nikolai Matni:
Robust Model Predictive Control with Polytopic Model Uncertainty through System Level Synthesis. CoRR abs/2203.11375 (2022) - [i52]Anastasios Tsiamis, Ingvar M. Ziemann, Manfred Morari, Nikolai Matni, George J. Pappas:
Learning to Control Linear Systems can be Hard. CoRR abs/2205.14035 (2022) - [i51]Lars Lindemann, Lejun Jiang, Nikolai Matni, George J. Pappas:
Risk of Stochastic Systems for Temporal Logic Specifications. CoRR abs/2205.14523 (2022) - [i50]Daniel Pfrommer, Thomas T. C. K. Zhang, Stephen Tu, Nikolai Matni:
TaSIL: Taylor Series Imitation Learning. CoRR abs/2205.14812 (2022) - [i49]Haoze Wu, Teruhiro Tagomori, Alexander Robey, Fengjun Yang, Nikolai Matni, George J. Pappas, Hamed Hassani, Corina S. Pasareanu, Clark W. Barrett:
Toward Certified Robustness Against Real-World Distribution Shifts. CoRR abs/2206.03669 (2022) - [i48]Ingvar M. Ziemann, Anastasios Tsiamis, Henrik Sandberg, Nikolai Matni:
How are policy gradient methods affected by the limits of control? CoRR abs/2206.06863 (2022) - [i47]Anastasios Tsiamis, Ingvar M. Ziemann, Nikolai Matni, George J. Pappas:
Statistical Learning Theory for Control: A Finite Sample Perspective. CoRR abs/2209.05423 (2022) - [i46]Shaoru Chen, Ning-Yuan Li, Victor M. Preciado, Nikolai Matni:
Robust Model Predictive Control of Time-Delay Systems through System Level Synthesis. CoRR abs/2209.11841 (2022) - [i45]David Brandfonbrener, Stephen Tu, Avi Singh, Stefan Welker, Chad Boodoo, Nikolai Matni, Jake Varley:
Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning. CoRR abs/2210.02343 (2022) - [i44]Fengjun Yang, Fernando Gama, Somayeh Sojoudi, Nikolai Matni:
Distributed Optimal Control of Graph Symmetric Systems via Graph Filters. CoRR abs/2210.15847 (2022) - [i43]Kong Yao Chee, M. Ani Hsieh, Nikolai Matni:
Learning-enhanced Nonlinear Model Predictive Control using Knowledge-based Neural Ordinary Differential Equations and Deep Ensembles. CoRR abs/2211.13829 (2022) - [i42]Thomas T. C. K. Zhang, Katie Kang, Bruce D. Lee, Claire J. Tomlin, Sergey Levine, Stephen Tu, Nikolai Matni:
Multi-Task Imitation Learning for Linear Dynamical Systems. CoRR abs/2212.00186 (2022) - 2021
- [c57]Alexander Robey, Lars Lindemann, Stephen Tu, Nikolai Matni:
Learning Robust Hybrid Control Barrier Functions for Uncertain Systems. ADHS 2021: 1-6 - [c56]Lars Lindemann, Nikolai Matni, George J. Pappas:
STL Robustness Risk over Discrete-Time Stochastic Processes. CDC 2021: 1329-1335 - [c55]Fengjun Yang, Nikolai Matni:
Communication Topology Co-Design in Graph Recurrent Neural Network based Distributed Control. CDC 2021: 3619-3626 - [c54]Bernadette Bucher, Karl Schmeckpeper, Nikolai Matni, Kostas Daniilidis:
An Adversarial Objective for Scalable Exploration. IROS 2021: 2670-2677 - [c53]Anton Xue, Nikolai Matni:
Data-Driven System Level Synthesis. L4DC 2021: 189-200 - [c52]Jingxi Xu, Bruce D. Lee, Nikolai Matni, Dinesh Jayaraman:
How Are Learned Perception-Based Controllers Impacted by the Limits of Robust Control? L4DC 2021: 954-966 - [i41]Alexander Robey, Lars Lindemann, Stephen Tu, Nikolai Matni:
Learning Robust Hybrid Control Barrier Functions for Uncertain Systems. CoRR abs/2101.06492 (2021) - [i40]Stephen Tu, Alexander Robey, Nikolai Matni:
Closing the Closed-Loop Distribution Shift in Safe Imitation Learning. CoRR abs/2102.09161 (2021) - [i39]Jingxi Xu, Bruce D. Lee, Nikolai Matni, Dinesh Jayaraman:
How Are Learned Perception-Based Controllers Impacted by the Limits of Robust Control? CoRR abs/2104.00827 (2021) - [i38]Lars Lindemann, Nikolai Matni, George J. Pappas:
STL Robustness Risk over Discrete-Time Stochastic Processes. CoRR abs/2104.01503 (2021) - [i37]Fengjun Yang, Nikolai Matni:
Communication Topology Co-Design in Graph Recurrent Neural Network Based Distributed Control. CoRR abs/2104.13868 (2021) - [i36]Shaoru Chen, Nikolai Matni, Manfred Morari, Victor M. Preciado:
System Level Synthesis-based Robust Model Predictive Control through Convex Inner Approximation. CoRR abs/2111.05509 (2021) - [i35]Bruce D. Lee, Thomas T. C. K. Zhang, Hamed Hassani, Nikolai Matni:
Adversarial Tradeoffs in Linear Inverse Problems and Robust State Estimation. CoRR abs/2111.08864 (2021) - [i34]Lars Lindemann, Alexander Robey, Lejun Jiang, Stephen Tu, Nikolai Matni:
Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations. CoRR abs/2111.09971 (2021) - [i33]Bibit Bianchini, Mathew Halm, Nikolai Matni, Michael Posa:
Generalization Bounded Implicit Learning of Nearly Discontinuous Functions. CoRR abs/2112.06881 (2021) - [i32]Thomas T. C. K. Zhang, Stephen Tu, Nicholas M. Boffi, Jean-Jacques E. Slotine, Nikolai Matni:
Adversarially Robust Stability Certificates can be Sample-Efficient. CoRR abs/2112.10690 (2021) - [i31]Carmen Amo Alonso, Fengjun Yang, Nikolai Matni:
Data-driven Distributed and Localized Model Predictive Control. CoRR abs/2112.12229 (2021) - 2020
- [j8]Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu:
On the Sample Complexity of the Linear Quadratic Regulator. Found. Comput. Math. 20(4): 633-679 (2020) - [j7]Salar Fattahi, Nikolai Matni, Somayeh Sojoudi:
Efficient Learning of Distributed Linear-Quadratic Control Policies. SIAM J. Control. Optim. 58(5): 2927-2951 (2020) - [c51]Nikolai Matni, Anish A. Sarma:
Robust Performance Guarantees for System Level Synthesis. ACC 2020: 779-786 - [c50]Natalie Bernat, Jiexin Chen, Nikolai Matni, John Doyle:
The driver and the engineer: Reinforcement learning and robust control. ACC 2020: 3932-3939 - [c49]Shaoru Chen, Han Wang, Manfred Morari, Victor M. Preciado, Nikolai Matni:
Robust Closed-loop Model Predictive Control via System Level Synthesis. CDC 2020: 2152-2159 - [c48]Alexander Robey, Haimin Hu, Lars Lindemann, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni:
Learning Control Barrier Functions from Expert Demonstrations. CDC 2020: 3717-3724 - [c47]Carmen Amo Alonso, Nikolai Matni:
Distributed and Localized Closed Loop Model Predictive Control via System Level Synthesis. CDC 2020: 5598-5605 - [c46]Carmen Amo Alonso, Nikolai Matni, James Anderson:
Explicit Distributed and Localized Model Predictive Control via System Level Synthesis. CDC 2020: 5606-5613 - [c45]Nicholas M. Boffi, Stephen Tu, Nikolai Matni, Jean-Jacques E. Slotine, Vikas Sindhwani:
Learning Stability Certificates from Data. CoRL 2020: 1341-1350 - [c44]Lars Lindemann, Haimin Hu, Alexander Robey, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni:
Learning Hybrid Control Barrier Functions from Data. CoRL 2020: 1351-1370 - [c43]Sangdon Park, Osbert Bastani, Nikolai Matni, Insup Lee:
PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction. ICLR 2020 - [c42]Laura Jarin-Lipschitz, Rebecca Li, Ty Nguyen, Vijay Kumar, Nikolai Matni:
Robust, Perception Based Control with Quadrotors. IROS 2020: 7737-7743 - [c41]Sarah Dean, Nikolai Matni, Benjamin Recht, Vickie Ye:
Robust Guarantees for Perception-Based Control. L4DC 2020: 350-360 - [c40]Anastasios Tsiamis, Nikolai Matni, George J. Pappas:
Sample Complexity of Kalman Filtering for Unknown Systems. L4DC 2020: 435-444 - [i30]Sangdon Park, Osbert Bastani, Nikolai Matni, Insup Lee:
PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction. CoRR abs/2001.00106 (2020) - [i29]Bernadette Bucher, Karl Schmeckpeper, Nikolai Matni, Kostas Daniilidis:
Action for Better Prediction. CoRR abs/2003.06082 (2020) - [i28]Alexander Robey, Haimin Hu, Lars Lindemann, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni:
Learning Control Barrier Functions from Expert Demonstrations. CoRR abs/2004.03315 (2020) - [i27]Carmen Amo Alonso, Nikolai Matni, James Anderson:
Explicit Distributed and Localized Model Predictive Control via System Level Synthesis. CoRR abs/2005.13807 (2020) - [i26]Laura Jarin-Lipschitz, Rebecca Li, Ty Nguyen, Vijay Kumar, Nikolai Matni:
Evaluating Robust, Perception Based Control with Quadrotors. CoRR abs/2007.04220 (2020) - [i25]Nicholas M. Boffi, Stephen Tu, Nikolai Matni, Jean-Jacques E. Slotine, Vikas Sindhwani:
Learning Stability Certificates from Data. CoRR abs/2008.05952 (2020) - [i24]Lars Lindemann, Haimin Hu, Alexander Robey, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni:
Learning Hybrid Control Barrier Functions from Data. CoRR abs/2011.04112 (2020) - [i23]Anton Xue, Nikolai Matni:
Data-Driven System Level Synthesis. CoRR abs/2011.10674 (2020)
2010 – 2019
- 2019
- [j6]James Anderson, John C. Doyle, Steven H. Low, Nikolai Matni:
System level synthesis. Annu. Rev. Control. 47: 364-393 (2019) - [j5]Yuh-Shyang Wang, Nikolai Matni, John C. Doyle:
A System-Level Approach to Controller Synthesis. IEEE Trans. Autom. Control. 64(10): 4079-4093 (2019) - [c39]Sarah Dean, Stephen Tu, Nikolai Matni, Benjamin Recht:
Safely Learning to Control the Constrained Linear Quadratic Regulator. ACC 2019: 5582-5588 - [c38]Salar Fattahi, Nikolai Matni, Somayeh Sojoudi:
Learning Sparse Dynamical Systems from a Single Sample Trajectory. CDC 2019: 2682-2689 - [c37]Nikolai Matni, Alexandre Proutière, Anders Rantzer, Stephen Tu:
From self-tuning regulators to reinforcement learning and back again. CDC 2019: 3724-3740 - [c36]Nikolai Matni, Stephen Tu:
A Tutorial on Concentration Bounds for System Identification. CDC 2019: 3741-3749 - [i22]James Anderson, Nikolai Matni, Yuxiao Chen:
Sparsity Preserving Discretization With Error Bounds. CoRR abs/1903.11267 (2019) - [i21]James Anderson, John C. Doyle, Steven H. Low, Nikolai Matni:
System Level Synthesis. CoRR abs/1904.01634 (2019) - [i20]