Safe Learning for Control using Control Lyapunov Functions and Control Barrier Functions: A Review

[1]  Paulo Tabuada,et al.  Control Barrier Functions: Theory and Applications , 2019, 2019 18th European Control Conference (ECC).

[2]  Bayu Jayawardhana,et al.  Stabilization with guaranteed safety using Control Lyapunov-Barrier Function , 2016, Autom..

[3]  Ofir Nachum,et al.  A Lyapunov-based Approach to Safe Reinforcement Learning , 2018, NeurIPS.

[4]  Jan Peters,et al.  Model learning for robot control: a survey , 2011, Cognitive Processing.

[5]  Sylvain Calinon,et al.  A Survey on Policy Search Algorithms for Learning Robot Controllers in a Handful of Trials , 2018, IEEE Transactions on Robotics.

[6]  Jaime F. Fisac,et al.  Reachability-based safe learning with Gaussian processes , 2014, 53rd IEEE Conference on Decision and Control.

[7]  Frank Allgöwer,et al.  CONSTRUCTIVE SAFETY USING CONTROL BARRIER FUNCTIONS , 2007 .

[8]  Shaoshuai Mou,et al.  Neural Certificates for Safe Control Policies , 2020, ArXiv.

[9]  Claire J. Tomlin,et al.  Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics , 2020, 2021 American Control Conference (ACC).

[10]  Massimo Franceschetti,et al.  Control Barriers in Bayesian Learning of System Dynamics , 2020, IEEE Transactions on Automatic Control.

[11]  Gábor Orosz,et al.  End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks , 2019, AAAI.

[12]  Yisong Yue,et al.  Learning for Safety-Critical Control with Control Barrier Functions , 2019, L4DC.

[13]  Andreas Krause,et al.  Safe Model-based Reinforcement Learning with Stability Guarantees , 2017, NIPS.

[14]  Lukas Hewing,et al.  Learning-Based Model Predictive Control: Toward Safe Learning in Control , 2020, Annu. Rev. Control. Robotics Auton. Syst..

[15]  Sandra Hirche,et al.  Learning stable Gaussian process state space models , 2017, 2017 American Control Conference (ACC).

[16]  Aaron D. Ames,et al.  Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems* , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[17]  Matteo Saveriano,et al.  Learning Barrier Functions for Constrained Motion Planning with Dynamical Systems , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[18]  Felix Berkenkamp,et al.  Safe Exploration in Reinforcement Learning: Theory and Applications in Robotics , 2019 .

[19]  Claire J. Tomlin,et al.  Guaranteed Safe Online Learning via Reachability: tracking a ground target using a quadrotor , 2012, 2012 IEEE International Conference on Robotics and Automation.

[20]  Bahare Kiumarsi,et al.  Safe reinforcement learning: A control barrier function optimization approach , 2020, International Journal of Robust and Nonlinear Control.

[21]  Pieter Abbeel,et al.  An Application of Reinforcement Learning to Aerobatic Helicopter Flight , 2006, NIPS.

[22]  George J. Pappas,et al.  Control Barrier Functions for Unknown Nonlinear Systems using Gaussian Processes* , 2020, 2020 59th IEEE Conference on Decision and Control (CDC).

[23]  S. Streif,et al.  A reinforcement learning method with closed-loop stability guarantee , 2020 .

[24]  Andreas Krause,et al.  Safe learning of regions of attraction for uncertain, nonlinear systems with Gaussian processes , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[25]  Stefano Stramigioli,et al.  The Safety of Domestic Robotics: A Survey of Various Safety-Related Publications , 2014, IEEE Robotics & Automation Magazine.

[26]  Javier García,et al.  A comprehensive survey on safe reinforcement learning , 2015, J. Mach. Learn. Res..

[27]  Samuel Coogan,et al.  Synthesis of Control Barrier Functions Using a Supervised Machine Learning Approach , 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[28]  R. Freeman,et al.  Control Lyapunov functions: new ideas from an old source , 1996, Proceedings of 35th IEEE Conference on Decision and Control.

[29]  Andrew G. Barto,et al.  Lyapunov Design for Safe Reinforcement Learning , 2003, J. Mach. Learn. Res..

[30]  Massimo Franceschetti,et al.  Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics , 2020, L4DC.

[31]  Kristin Ytterstad Pettersen,et al.  Neural Network-Based Model Predictive Control with Input-to-State Stability , 2021, 2021 American Control Conference (ACC).

[32]  Tommaso Mannucci,et al.  Safe Exploration Algorithms for Reinforcement Learning Controllers , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[33]  Dimos V. Dimarogonas,et al.  Learning Control Barrier Functions from Expert Demonstrations , 2020, 2020 59th IEEE Conference on Decision and Control (CDC).

[34]  Li Wang,et al.  Safe Learning of Quadrotor Dynamics Using Barrier Certificates , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[35]  Mohammad Ghavamzadeh,et al.  Lyapunov-based Safe Policy Optimization for Continuous Control , 2019, ArXiv.

[36]  Antonios Gasteratos,et al.  Safety bounds in human robot interaction: A survey , 2020 .

[37]  David Muñoz de la Peña,et al.  Robust learning-based MPC for nonlinear constrained systems , 2020, Autom..

[38]  Koushil Sreenath,et al.  Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics , 2021, 2021 American Control Conference (ACC).

[39]  Panagiotis D. Christofides,et al.  Control Lyapunov-Barrier function-based predictive control of nonlinear processes using machine learning modeling , 2020, Comput. Chem. Eng..

[40]  Eduardo Sontag.  A universal construction of Artstein's theorem on nonlinear stabilization , 1989 .

[41]  Sergey Levine,et al.  Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection , 2016, Int. J. Robotics Res..

[42]  Z. Artstein.  Stabilization with relaxed controls , 1983 .

[43]  Mario Zanon,et al.  Safe Reinforcement Learning Using Robust MPC , 2019, IEEE Transactions on Automatic Control.

[44]  Koushil Sreenath,et al.  Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions , 2020, Robotics: Science and Systems.

[45]  David D. Fan,et al.  Bayesian Learning-Based Adaptive Control for Safety Critical Systems , 2019, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[46]  Taolue Chen,et al.  Learning safe neural network controllers with barrier certificates , 2020, Formal Aspects of Computing.