[2510.14959] CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functions

Computer Science > Robotics arXiv:2510.14959 (cs) [Submitted on 16 Oct 2025 (v1), last revised 5 Mar 2026 (this version, v3)] Title:CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functions Authors:Lizhi Yang, Blake Werner, Massimiliano de Sa, Aaron D. Ames View a PDF of the paper titled CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functions, by Lizhi Yang and 3 other authors View PDF HTML (experimental) Abstract:Reinforcement learning (RL), while powerful and expressive, can often prioritize performance at the expense of safety. Yet safety violations can lead to catastrophic outcomes in real-world deployments. Control Barrier Functions (CBFs) offer a principled method to enforce dynamic safety -- traditionally deployed online via safety filters. While the result is safe behavior, the fact that the RL policy does not have knowledge of the CBF can lead to conservative behaviors. This paper proposes CBF-RL, a framework for generating safe behaviors with RL by enforcing CBFs in training. CBF-RL has two key attributes: (1) minimally modifying a nominal RL policy to encode safety constraints via a CBF term, (2) and safety filtering of the policy rollouts in training. Theoretically, we prove that continuous-time safety filters can be deployed via closed-form expressions on discrete-time roll-outs. Practically, we demonstrate that CBF-RL internalizes the safety constraints in the learned policy -- both enforcing...