On the Optimality, Stability, and Feasibility of Control Barrier Functions: An Adaptive Learning-Based Approach

5 May 2023

Papers citing "On the Optimality, Stability, and Feasibility of Control Barrier Functions: An Adaptive Learning-Based Approach"

20 / 20 papers shown

Title
Learning-Enhanced Safeguard Control for High-Relative-Degree Systems: Robust Optimization under Disturbances and Faults Xinyang Wang Hongwei Zhang Shimin Wang Wei Xiao M. Guay 106 0 0 28 Jan 2025
Safe Reinforcement Learning Using Robust Control Barrier Functions Y. Emam Gennaro Notomista Paul Glotfelter Z. Kira M. Egerstedt OffRL 57 42 0 11 Oct 2021
A Theoretical Overview of Neural Contraction Metrics for Learning-based Control with Guaranteed Stability Hiroyasu Tsukamoto Soon-Jo Chung Jean-Jacques E. Slotine Chuchu Fan 50 11 0 02 Oct 2021
Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach Hiroyasu Tsukamoto Soon-Jo Chung OOD 79 34 0 25 Feb 2021
Sufficient Conditions for Feasibility of Optimal Control Problems Using Control Barrier Functions Wei Xiao C. Belta Christos G. Cassandras 69 67 0 16 Nov 2020
Safe Reinforcement Learning via Curriculum Induction M. Turchetta Andrey Kolobov S. Shah Andreas Krause Alekh Agarwal 47 93 0 22 Jun 2020
Neural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach Hiroyasu Tsukamoto Soon-Jo Chung 66 59 0 08 Jun 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 58 185 0 16 Apr 2020
Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks Liyuan Zheng Yuanyuan Shi Lillian J. Ratliff Baosen Zhang 41 20 0 20 Mar 2020
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning Tabish Rashid Mikayel Samvelyan Christian Schroeder de Witt Gregory Farquhar Jakob N. Foerster Shimon Whiteson 127 820 0 19 Mar 2020
Differentiable Convex Optimization Layers Akshay Agrawal Brandon Amos Shane T. Barratt Stephen P. Boyd Steven Diamond Zico Kolter 102 665 0 28 Oct 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 85 624 0 21 Mar 2019
The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamical Systems Spencer M. Richards Felix Berkenkamp Andreas Krause 68 229 0 02 Aug 2018
A Lyapunov-based Approach to Safe Reinforcement Learning Yinlam Chow Ofir Nachum Edgar A. Duénez-Guzmán Mohammad Ghavamzadeh 165 508 0 20 May 2018
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning Tabish Rashid Mikayel Samvelyan Christian Schroeder de Witt Gregory Farquhar Jakob N. Foerster Shimon Whiteson 169 1,677 0 30 Mar 2018
Safe Reinforcement Learning via Shielding Mohammed Alshiekh Roderick Bloem Rüdiger Ehlers Bettina Könighofer S. Niekum Ufuk Topcu 87 693 0 29 Aug 2017
Safe Model-based Reinforcement Learning with Stability Guarantees Felix Berkenkamp M. Turchetta Angela P. Schoellig Andreas Krause 198 854 0 23 May 2017
OptNet: Differentiable Optimization as a Layer in Neural Networks Brandon Amos J. Zico Kolter 173 972 0 01 Mar 2017
Safe and Efficient Off-Policy Reinforcement Learning Rémi Munos T. Stepleton Anna Harutyunyan Marc G. Bellemare OffRL 140 617 0 08 Jun 2016
Continuous control with deep reinforcement learning Timothy Lillicrap Jonathan J. Hunt Alexander Pritzel N. Heess Tom Erez Yuval Tassa David Silver Daan Wierstra 334 13,295 0 09 Sep 2015