Learning Safe Neural Network Controllers with Barrier Certificates

18 September 2020

Papers citing "Learning Safe Neural Network Controllers with Barrier Certificates"

24 / 24 papers shown

Title
Automated and Formal Synthesis of Neural Barrier Certificates for Dynamical Models Andrea Peruffo D. Ahmed Alessandro Abate 38 15 0 07 Jul 2020
Neural Lyapunov Control Ya-Chien Chang Nima Roohi Sicun Gao 71 305 0 01 May 2020
Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions Jason J. Choi F. Castañeda Claire Tomlin Koushil Sreenath 43 185 0 16 Apr 2020
NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systems Hoang-Dung Tran Xiaodong Yang Diego Manzanas Lopez Patrick Musau L. V. Nguyen Weiming Xiang Stanley Bak Taylor T. Johnson 95 242 0 12 Apr 2020
Exactly Computing the Local Lipschitz Constant of ReLU Networks Matt Jordan A. Dimakis 52 112 0 02 Mar 2020
Training Neural Network Controllers Using Control Barrier Functions in the Presence of Disturbances Shakiba Yaghoubi Georgios Fainekos S. Sankaranarayanan 60 42 0 18 Jan 2020
Learning Stable Deep Dynamics Models Gaurav Manek J. Zico Kolter 54 192 0 17 Jan 2020
Learning for Safety-Critical Control with Control Barrier Functions Andrew J. Taylor Andrew W. Singletary Yisong Yue Aaron D. Ames 106 243 0 20 Dec 2019
Case Study: Verifying the Safety of an Autonomous Racing Car with a Neural Network Controller Radoslav Ivanov Taylor J. Carpenter James Weimer Rajeev Alur George J. Pappas Insup Lee 64 82 0 24 Oct 2019
An Inductive Synthesis Framework for Verifiable Reinforcement Learning He Zhu Zikang Xiong Stephen Magill Suresh Jagannathan 58 97 0 16 Jul 2019
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks Richard Cheng G. Orosz R. Murray J. W. Burdick 77 621 0 21 Mar 2019
Safe Policy Synthesis in Multi-Agent POMDPs via Discrete-Time Barrier Functions M. Ahmadi Andrew W. Singletary J. W. Burdick Aaron D. Ames 55 69 0 19 Mar 2019
Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems Andrew J. Taylor Victor D. Dorobantu Hoang Minh Le Yisong Yue Aaron D. Ames 173 79 0 04 Mar 2019
Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification Jianlin Li Pengfei Yang Jiangchao Liu Liqian Chen Xiaowei Huang Lijun Zhang AAML 61 80 0 26 Feb 2019
Formal Verification of Neural Network Controlled Autonomous Systems Xiaowu Sun Haitham Khedr Yasser Shoukry 73 138 0 31 Oct 2018
The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamical Systems Spencer M. Richards Felix Berkenkamp Andreas Krause 48 229 0 02 Aug 2018
Towards Fast Computation of Certified Robustness for ReLU Networks Tsui-Wei Weng Huan Zhang Hongge Chen Zhao Song Cho-Jui Hsieh Duane S. Boning Inderjit S. Dhillon Luca Daniel AAML 108 695 0 25 Apr 2018
Reasoning about Safety of Learning-Enabled Components in Autonomous Cyber-physical Systems Cumhur Erkan Tuncali J. Kapinski Hisahiro Ito Jyotirmoy V. Deshmukh 57 42 0 11 Apr 2018
Output Reachable Set Estimation and Verification for Multi-Layer Neural Networks Weiming Xiang Hoang-Dung Tran Taylor T. Johnson 108 294 0 09 Aug 2017
Neural networks and rational functions Matus Telgarsky 60 81 0 11 Jun 2017
Safe Model-based Reinforcement Learning with Stability Guarantees Felix Berkenkamp M. Turchetta Angela P. Schoellig Andreas Krause 176 852 0 23 May 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 318 1,868 0 03 Feb 2017
Benchmarking Deep Reinforcement Learning for Continuous Control Yan Duan Xi Chen Rein Houthooft John Schulman Pieter Abbeel OffRL 79 1,694 0 22 Apr 2016
Continuous control with deep reinforcement learning Timothy Lillicrap Jonathan J. Hunt Alexander Pritzel N. Heess Tom Erez Yuval Tassa David Silver Daan Wierstra 320 13,248 0 09 Sep 2015