# Learning Safety-Guaranteed, Non-Greedy Control Barrier Functions Using Reinforcement Learning

arXiv: https://arxiv.org/abs/2602.00366 · HTML: https://mlsanity.com/paper/2602.00366/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 30 Jan 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Safe Model-based Reinforcement Learning with Stability Guarantees](https://mlsanity.com/paper/1705.08551/) (2017)
- [A Lyapunov-based Approach to Safe Reinforcement Learning](https://mlsanity.com/paper/1805.07708/) (2018)
- [End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks](https://mlsanity.com/paper/1903.08792/) (2019)
- [Control Barrier Function Based Quadratic Programs for Safety Critical Systems](https://mlsanity.com/paper/1609.06408/) (2016)
- [Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methods](https://mlsanity.com/paper/2202.11762/) (2022)
- [Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function](https://mlsanity.com/paper/2007.11718/) (2020)
- [Safe Control Synthesis via Input Constrained Control Barrier Functions](https://mlsanity.com/paper/2104.01704/) (2021)
- [Enhancing Feasibility and Safety of Nonlinear Model Predictive Control with Discrete-Time Control Barrier Functions](https://mlsanity.com/paper/2105.10596/) (2021)
- [Predictive Control Barrier Functions for Online Safety Critical Control](https://mlsanity.com/paper/2204.00208/) (2022)

## Built on by (most cited first, 1 of 1)

- [Memory-Efficient Meta-Reinforcement Learning for Adaptive Safety-Critical Control in Adversarial Spacecraft Proximity Operations](https://mlsanity.com/paper/2606.17414/) (2026)

## Abstract (arXiv)

Spacecraft rendezvous and proximity operations (RPO) pose safety risks to high-value assets, so formal safety guarantees are critical. Yet conservative safety controllers can reduce mission efficiency. We propose a unified two-stage reinforcement learning (RL) framework that addresses two complementary limitations of Input-Constrained Control Barrier Functions (ICCBFs) for safety-critical, fuel-limited spacecraft control. Given a certified safe set S, ICCBFs guarantee forward invariance of an inner set C* under input bounds, but the resulting per-step quadratic programme (QP) is greedy and fuel-inefficient within C*, and recoverable states outside C* are conservatively discarded. Stage 1 learns state-dependent class-K-infinity parameters that adapt ICCBF/CLF decay rates, embedding long-horizon cost awareness while preserving invariance in C*. Stage 2 learns a residual barrier h_RL(x) that certifies recoverability for a subset of S minus C*. At run time, the controller selects the appropriate barrier formulation (Stage 1 or Stage 2) and solves a lightweight ZOH QP. Both stages are trained with PPO using rewards that penalise constraint violations, control effort, and task metrics. We evaluate three benchmarks: cruise control, spacecraft rendezvous with a rotating target, and inspection that maximises observability subject to keep-in and keep-out zone constraints. Across test cases, the method reduces median fuel relative to ICCBF baselines by 12 to 25 percent and increases the fraction of trajectories that remain in S by 7 to 8 percent, while retaining real-time QP complexity.