# Meta-Reinforcement Learning for Robust and Non-greedy Control Barrier Functions in Spacecraft Proximity Operations

arXiv: https://arxiv.org/abs/2602.07335 · HTML: https://mlsanity.com/paper/2602.07335/

## Key facts

- Cited by 2 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 16 of its 29 references that are in the map.
- First posted on arXiv 7 Feb 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, 16 of 16)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [A Tutorial on Meta-Reinforcement Learning](https://mlsanity.com/paper/2301.08028/) (2023)
- [Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function](https://mlsanity.com/paper/2007.11718/) (2020)
- [Input-to-State Safety With Control Barrier Functions](https://mlsanity.com/paper/1803.03035/) (2018)
- [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)
- [High-order Barrier Functions: Robustness, Safety and Performance-Critical Control](https://mlsanity.com/paper/2104.00101/) (2021)
- [Adaptive Guidance and Integrated Navigation with Reinforcement Meta-Learning](https://mlsanity.com/paper/1904.09865/) (2019)
- [Control Barrier Functions in Sampled-Data Systems](https://mlsanity.com/paper/2103.03677/) (2021)
- [Learning to Refine Input Constrained Control Barrier Functions via Uncertainty-Aware Online Parameter Adaptation](https://mlsanity.com/paper/2409.14616/) (2024)
- [Predictive Control Barrier Functions for Online Safety Critical Control](https://mlsanity.com/paper/2204.00208/) (2022)
- [Control Barrier Functions for Sampled-Data Systems with Input Delays](https://mlsanity.com/paper/2005.06418/) (2020)
- [Robust Control Barrier Functions for Sampled-Data Systems](https://mlsanity.com/paper/2309.08050/) (2023)
- [Feasibility-Guaranteed Safety-Critical Control with Applications to Heterogeneous Platoons](https://mlsanity.com/paper/2310.00238/) (2023)
- [Run Time Assured Reinforcement Learning for Six Degree-of-Freedom Spacecraft Inspection](https://mlsanity.com/paper/2406.11795/) (2024)

## Built on by (most cited first, 2 of 2)

- [Memory-Efficient Meta-Reinforcement Learning for Adaptive Safety-Critical Control in Adversarial Spacecraft Proximity Operations](https://mlsanity.com/paper/2606.17414/) (2026)
- [SCoCaT: Success Conditioned Constrained Reinforcement Learning for Spacecraft Docking](https://mlsanity.com/paper/2609.06061/) (2026)

## Abstract (arXiv)

Autonomous spacecraft inspection and docking missions require controllers that can guarantee safety under thrust constraints and uncertainty. Input-constrained control barrier functions (ICCBFs) provide a framework for safety certification under bounded actuation; however, conventional ICCBF formulations can be overly conservative and exhibit limited robustness to uncertainty, leading to high fuel consumption and reduced mission feasibility. This paper proposes a framework in which the full hierarchy of class-K functions defining the ICCBF recursion is parameterized and learned, enabling localized shaping of the safe set and reduced conservatism. A control margin is computed efficiently using differential algebra to enable the learned continuous-time ICCBFs to be implemented on time-sampled dynamical systems typical of spacecraft proximity operations. A meta-reinforcement learning scheme is developed to train a policy that generates ICCBF parameters over a distribution of hidden physical parameters and uncertainties, using both multilayer perceptron (MLP) and recurrent neural network (RNN) architectures. Simulation results on cruise control, spacecraft inspection, and docking scenarios demonstrate that the proposed approach maintains safety while reducing fuel consumption and improving feasibility relative to fixed class-K ICCBFs, with the RNN showing a particularly strong advantage in the more complex inspection case.