mlsanityData to 7 Oct 2026

V-OCBF: Learning Safety Filters from Offline Data via Value-Guided Offline Control Barrier Functions

arXiv 2512.10822 ↗First posted 11 Dec 2025cs.AI cs.ROcited by 5

On the map: Offline RL and imitation learning from data → Safe offline RL

Abstract (arXiv)

Ensuring safety in autonomous systems requires controllers that aim to satisfy state-wise constraints without relying on online interaction.While existing Safe Offline RL methods typically enforce soft expected-cost constraints, they struggle to ensure strict state-wise safety. Conversely, Control Barrier Functions (CBFs) offer a principled mechanism to enforce forward invariance, but often rely on expert-designed barrier functions or knowledge of the system dynamics. We introduce Value-Guided Offline Control Barrier Functions (V-OCBF), a framework that learns a neural CBF entirely from offline demonstrations. Unlike prior approaches, V-OCBF does not assume access to the dynamics model; instead, it derives a recursive finite-difference barrier update, enabling model-free learning of a barrier that propagates safety information over time. Moreover, V-OCBF incorporates an expectile-based objective that avoids querying the barrier on out-of-distribution actions and restricts updates to the dataset-supported action set. The learned barrier is then used with a Quadratic Program (QP) formulation to synthesize real-time safe control. Across multiple case studies, V-OCBF yields substantially fewer safety violations than baseline methods while maintaining strong task performance, highlighting its scalability for offline synthesis of safety-critical controllers without online interaction or hand-engineered barriers.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Safe offline RL, one of 13 directions in Offline RL and imitation learning from data. Builds on 35 of its 52 references that are in the map. First posted on arXiv 11 Dec 2025.

Builds on

35 of 52 references.

Closest ancestors · 7

Same direction, newest first

  1. 2024SAFE-GIL: SAFEty Guided Imitation Learning for Robotic Systems8 Apr
  2. 2024Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model19 Jan
  3. 2023Datasets and Benchmarks for Offline Safe Reinforcement Learning15 Jun
  4. 2023Safe Offline Reinforcement Learning with Real-Time Budget Constraints1 Jun
  5. 2023Constrained Decision Transformer for Offline Safe Reinforcement Learning14 Feb
  6. 2021Constraints Penalized Q-learning for Safe Offline Reinforcement Learning19 Jul
  7. 2020Learning Control Barrier Functions from Expert Demonstrations7 Apr

Other references · 28

  1. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  2. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  3. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  4. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,528
  5. 2021Offline Reinforcement Learning with Implicit Q-Learningcited by 1,245
  6. 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,023
  7. 2017Constrained Policy Optimizationcited by 928
  8. 2019Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reductioncited by 833
  9. 2017Safe Reinforcement Learning via Shieldingcited by 427
  10. 2018Reward Constrained Policy Optimizationcited by 397
  11. 2015Risk-Constrained Reinforcement Learning with Percentile Risk Criteriacited by 320
  12. 2020Responsive Safety in Reinforcement Learning by PID Lagrangian Methodscited by 302
16 more
  1. 2016Control Barrier Function Based Quadratic Programs for Safety Critical Systemscited by 261
  2. 2017Hamilton-Jacobi Reachability: A Brief Overview and Recent Advancescited by 186
  3. 2022Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methodscited by 148
  4. 2023Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmarkcited by 135
  5. 2021OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimationcited by 121
  6. 2021Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functionscited by 91
  7. 2022COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimationcited by 69
  8. 2021Robust Control Barrier-Value Functions for Safety-Critical Controlcited by 47
  9. 2023How to Train Your Neural Control Barrier Function: Learning Safety Filters for Complex Input-Constrained Systemscited by 40
  10. 2022When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learningcited by 31
  11. 2023In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution Statescited by 15
  12. 2023Exact Verification of ReLU Neural Control Barrier Functionscited by 14
  13. 2025A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systemscited by 12
  14. 2024Learning a Formally Verified Control Barrier Function in Stochastic Environmentcited by 11
  15. 2023Control Barrier Functions in Dynamic UAVs for Kinematic Obstacle Avoidance: A Collision Cone Approachcited by 6
  16. 2024Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learningcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Safe Flow Q-Learning: Offline Safe Reinforcement Learning with Reachability-Based Flow Policiescited by 3
  2. 2026Epigraph-Guided Flow Matching for Safe and Performant Offline Reinforcement Learningcited by 2
  3. 2026Any-Body Guard: Universal Safeguarding for Manipulation Policies via Action Maskingcited by 2
  4. 2026Discriminative Barrier Functions for Safe Adversarial Imitation Learning from Observationcited by 0
  5. 2026Foundations of Reinforcement Learning and Control:Connections and New Perspectivescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Learning Conservative Neural Control Barrier Functions from Offline Data
  2. 2022End-to-End Imitation Learning with Safety Guarantees using Control Barrier Functions
  3. 2023Safe Neural Control for Non-Affine Control Systems with Differentiable Control Barrier Functions
  4. 2026Individual Control Barrier Functions-Guided Diffusion Model for Safe Offline Multi-Agent Reinforcement Learning
  5. 2025Safety Certification in the Latent space using Control Barrier Functions and World Models
  6. 2023Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration
  7. 2024Latent Safety-Constrained Policy Approach for Safe Offline Reinforcement Learning
  8. 2020Safety Considerations in Deep Control Policies with Safety Barrier Certificates Under Uncertainty