# TRUST-UP: Trustworthy Reinforcement learning Using Safe Techniques for UAV Pursuit

arXiv: https://arxiv.org/abs/2411.17552 · HTML: https://mlsanity.com/paper/2411.17552/

## Key facts

- No citing papers in the map yet (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 17 of its 52 references that are in the map.
- First posted on arXiv 26 Nov 2024.

## 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, 17 of 17)

- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee](https://mlsanity.com/paper/2011.05869/) (2020)
- [Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds](https://mlsanity.com/paper/2205.06908/) (2022)
- [Safe Reinforcement Learning via Shielding under Partial Observability](https://mlsanity.com/paper/2204.00755/) (2022)
- [How to Train Your Neural Control Barrier Function: Learning Safety Filters for Complex Input-Constrained Systems](https://mlsanity.com/paper/2310.15478/) (2023)
- [Decentralized Multi-Agent Pursuit using Deep Reinforcement Learning](https://mlsanity.com/paper/2010.08193/) (2020)
- [Safe Exploration in Model-based Reinforcement Learning using Control Barrier Functions](https://mlsanity.com/paper/2104.08171/) (2021)
- [Integral Control Barrier Functions for Dynamically Defined Control Laws](https://mlsanity.com/paper/2006.16936/) (2020)
- [Knowledge Transfer from Simple to Complex: A Safe and Efficient Reinforcement Learning Framework for Autonomous Driving Decision-Making](https://mlsanity.com/paper/2410.14468/) (2024)
- [Safety of Dynamical Systems with Multiple Non-Convex Unsafe Sets Using Control Barrier Functions](https://mlsanity.com/paper/2106.06330/) (2021)
- [Image-Based Multi-UAV Tracking System in a Cluttered Environment](https://mlsanity.com/paper/2109.07079/) (2021)
- [State-action control barrier functions: Imposing safety on learning-based control with low online computational costs](https://mlsanity.com/paper/2312.11255/) (2023)
- [Adaptive Control of Time-Varying Parameter Systems with Asymptotic Tracking](https://mlsanity.com/paper/2007.11801/) (2020)
- [High Order Robust Adaptive Control Barrier Functions and Exponentially Stabilizing Adaptive Control Lyapunov Functions](https://mlsanity.com/paper/2203.01999/) (2022)
- [Safety Filter Design for Neural Network Systems via Convex Optimization](https://mlsanity.com/paper/2308.08086/) (2023)
- [On unifying control barrier and Lyapunov functions using QP and Sontag's formula with an application to tumor dynamics](https://mlsanity.com/paper/2403.14226/) (2024)
- [Safety Critical Control for Nonlinear Systems with Complex Input Constraints](https://mlsanity.com/paper/2408.09534/) (2024)

## Abstract (arXiv)

Reinforcement Learning (RL) enables autonomous aerial vehicles to adapt quickly and make efficient decisions, making it well-suited for dynamic urban air mobility operations. However, the lack of safety guarantees and transparency hinders the airworthiness certification of RL-based flight control systems, particularly in low-altitude urban environments with human presence. This paper proposes a trustworthy reinforcement learning algorithm that utilizes safe techniques to address the AI trustworthiness requirements for aviation safety, ensuring the transparent and certifiable deployment of RL in safety-critical aerial operations. Specifically, we proposed a Trustworthy Reinforcement learning Using Safe Techniques for UAV Pursuit (TRUST-UP), which consists of two key components: a safety filter constructed from Control Barrier Functions (CBFs) that transforms unsafe RL actions into provably safe flight commands, and a switching strategy that enhances feasibility while maintaining operational transparency. These components enable trustworthy AI deployment in urban airspace, satisfying technical robustness and transparency requirements for aviation certification. Simulation results demonstrate that TRUST-UP enables autonomous UAVs to safely navigate congested urban environments while maintaining human-interpretable decision logic. This work contributes toward certifiable and explainable AI frameworks for low-altitude aviation, addressing the critical need for trustworthy autonomous flight systems in future urban air mobility.