mlsanityData to 14 Oct 2026

A Control Barrier Function-Constrained Model Predictive Control Framework for Safe Reinforcement Learning

arXiv 2604.06463 ↗First posted 7 Apr 2026eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → Safe and constrained RL

Abstract (arXiv)

Ensuring safety under unknown and stochastic dynamics remains a significant challenge in reinforcement learning (RL). In this paper, we propose a model predictive control (MPC)-based safe RL framework, called Probabilistic Ensembles with CBF-constrained Trajectory Sampling (PECTS), to address this challenge. PECTS jointly learns stochastic system dynamics with probabilistic neural networks (PNNs) and control barrier functions (CBFs) with Lipschitz-bounded neural networks. Safety is enforced by incorporating learned CBF constraints into the MPC formulation while accounting for the model stochasticity. This enables probabilistic safety under model uncertainty. To solve the resulting MPC problem, we utilize a sampling-based optimizer together with a safe trajectory sampling method that discards unsafe trajectories based on the learned system model and CBF. We validate PECTS in various simulation studies, where it outperforms baseline methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods. Builds on 21 of its 27 references that are in the map. First posted on arXiv 7 Apr 2026.

Builds on

21 of 27 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Probabilistically safe and efficient model-based reinforcement learning1 Apr
  2. 2024Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical Systems26 Mar

Other references · 19

  1. 2017Constrained Policy Optimizationcited by 983
  2. 2018Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelscited by 774
  3. 2019Deep Dynamics Models for Learning Dexterous Manipulationcited by 206
  4. 2020Learning Control Barrier Functions from Expert Demonstrationscited by 92
  5. 2022Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environmentscited by 60
  6. 2021Safe Reinforcement Learning Using Robust Control Barrier Functionscited by 59
  7. 2022Constrained Update Projection Approach to Safe Policy Optimizationcited by 58
  8. 2023Direct Parameterization of Lipschitz-Bounded Deep Networkscited by 51
  9. 2020Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Functioncited by 49
  10. 2021MBRL-Lib: A Modular Library for Model-based Reinforcement Learningcited by 42
  11. 2021Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violationscited by 39
  12. 2023Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functionscited by 13
7 more
  1. 2022Learning a Better Control Barrier Functioncited by 6
  2. 2023Data-Efficient Control Barrier Function Refinementcited by 4
  3. 2022Path Integral Methods with Stochastic Control Barrier Functionscited by 2
  4. 2024Guaranteed-Safe MPPI Through Composite Control Barrier Functions for Efficient Sampling in Multi-Constrained Robotic Systemscited by 2
  5. 2025Safe Multi-Robotic Arm Interaction via 3D Convex Shapescited by 2
  6. 2024Bounding Stochastic Safety: Leveraging Freedman's Inequality with Discrete-Time Control Barrier Functionscited by 1
  7. 2025Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functionscited by 1

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Not cited yet.

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  1. 2025Safe model-based Reinforcement Learning via Model Predictive Control and Control Barrier Functions
  2. 2026CAPSULE: Control-Theoretic Action Perturbations for Safe Uncertainty-Aware Reinforcement Learning
  3. 2026Sampling-Based Safe Reinforcement Learning
  4. 2024Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems
  5. 2026Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamics
  6. 2023Trajectory Generation, Control, and Safety with Denoising Diffusion Probabilistic Models
  7. 2021Safe Continuous Control with Constrained Model-Based Policy Optimization
  8. 2025A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions