mlsanityData to 7 Oct 2026

Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions

arXiv 2305.09793 ↗First posted 16 May 2023cs.RO cs.AI cs.LGcited by 9

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Abstract (arXiv)

Reinforcement learning (RL) exhibits impressive performance when managing complicated control tasks for robots. However, its wide application to physical robots is limited by the absence of strong safety guarantees. To overcome this challenge, this paper explores the control Lyapunov barrier function (CLBF) to analyze the safety and reachability solely based on data without explicitly employing a dynamic model. We also proposed the Lyapunov barrier actor-critic (LBAC), a model-free RL algorithm, to search for a controller that satisfies the data-based approximation of the safety and reachability conditions. The proposed approach is demonstrated through simulation and real-world robot control experiments, i.e., a 2D quadrotor navigation task. The experimental findings reveal this approach's effectiveness in reachability and safety, surpassing other model-free RL methods.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 31 of its 49 references that are in the map. First posted on arXiv 16 May 2023.

Builds on

31 of 49 references.

References · 31

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2015Continuous control with deep reinforcement learningcited by 3,279
  3. 2017Constrained Policy Optimizationcited by 928
  4. 2018Reward Constrained Policy Optimizationcited by 397
  5. 2017Safe Model-based Reinforcement Learning with Stability Guaranteescited by 347
  6. 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 337
  7. 2018A Lyapunov-based Approach to Safe Reinforcement Learningcited by 278
  8. 2019End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Taskscited by 256
  9. 2019Deep Dynamics Models for Learning Dexterous Manipulationcited by 200
  10. 2020Recovery RL: Safe Reinforcement Learning with Learned Recovery Zonescited by 156
  11. 2019Lyapunov-based Safe Policy Optimization for Continuous Controlcited by 140
  12. 2013Risk-sensitive Reinforcement Learningcited by 129
19 more
  1. 2020Learning to be Safe: Deep RL with a Safety Criticcited by 109
  2. 2020Conservative Safety Critics for Explorationcited by 107
  3. 2021Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functionscited by 91
  4. 2017Uncertainty-Aware Reinforcement Learning for Collision Avoidancecited by 86
  5. 2016Safe Exploration in Finite Markov Decision Processes with Gaussian Processescited by 72
  6. 2021Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Controlcited by 68
  7. 2022Safe Reinforcement Learning by Imagining the Near Futurecited by 67
  8. 2017OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real Worldcited by 64
  9. 2020Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functionscited by 61
  10. 2019Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learningcited by 40
  11. 2020Actor-Critic Reinforcement Learning for Control with Stability Guaranteecited by 39
  12. 2020Neural Certificates for Safe Control Policiescited by 35
  13. 2020Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloadscited by 27
  14. 2020Multi-Layered Safety for Legged Robots via Control Barrier Functions and Model Predictive Controlcited by 20
  15. 2019Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systemscited by 19
  16. 2020Control Barrier Functions for Unknown Nonlinear Systems using Gaussian Processescited by 19
  17. 2018Barrier-Certified Adaptive Reinforcement Learning with Applications to Brushbot Navigationcited by 15
  18. 2020Reinforcement Learning Control of Constrained Dynamic Systems with Uniformly Ultimate Boundedness Stability Guaranteecited by 7
  19. 2020Smooth Converse Lyapunov-Barrier Theorems for Asymptotic Stability with Safety Constraints and Reach-Avoid-Stay Specificationscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 4; 2026: 1.

9 papers.

  1. 2023A Survey on Physics Informed Reinforcement Learning: Review and Open Problemscited by 22
  2. 2025CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functionscited by 8
  3. 2024Learning Control Barrier Functions and their application in Reinforcement Learning: A Surveycited by 3
  4. 2024Implicit Safe Set Algorithm for Provably Safe Reinforcement Learningcited by 3
  5. 2025Off Policy Lyapunov Stability in Reinforcement Learningcited by 2
  6. 2023Harmonic Control Lyapunov Barrier Functions for Constrained Optimal Control with Reach-Avoid Specificationscited by 0
  7. 2025Certificated Actor-Critic: Hierarchical Reinforcement Learning with Control Barrier Functions for Safe Navigationcited by 0
  8. 2025A Fast Initialization Method for Neural Network Controllers: A Case Study of Image-based Visual Servoing Control for the multicopter Interceptioncited by 0
  9. 2026Uncertainty-Aware and Temporally Regulated Expert Advice in Reinforcement Learning for Autonomous Drivingcited by 0