# AttentionSwarm: Reinforcement Learning with Attention Control Barier Function for Crazyflie Drones in Dynamic Environments

arXiv: https://arxiv.org/abs/2503.07376 · HTML: https://mlsanity.com/paper/2503.07376/

## 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 30 of its 37 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## 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, 30 of 30)

- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems](https://mlsanity.com/paper/1705.01292/) (2017)
- [Learning to be Safe: Deep RL with a Safety Critic](https://mlsanity.com/paper/2010.14603/) (2020)
- [Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control](https://mlsanity.com/paper/2103.02142/) (2021)
- [Deep Drone Racing: From Simulation to Reality with Domain Randomization](https://mlsanity.com/paper/1905.09727/) (2019)
- [Safe Reinforcement Learning with Model Uncertainty Estimates](https://mlsanity.com/paper/1810.08700/) (2018)
- [DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstacles](https://mlsanity.com/paper/2301.06512/) (2023)
- [Safe Reinforcement Learning for Legged Locomotion](https://mlsanity.com/paper/2203.02638/) (2022)
- [Demonstrating Agile Flight from Pixels without State Estimation](https://mlsanity.com/paper/2406.12505/) (2024)
- [NavRL: Learning Safe Flight in Dynamic Environments](https://mlsanity.com/paper/2409.15634/) (2024)
- [GCBF+: A Neural Graph Control Barrier Function Framework for Distributed Safe Multi-Agent Control](https://mlsanity.com/paper/2401.14554/) (2024)
- [X-MOBILITY: End-To-End Generalizable Navigation via World Modeling](https://mlsanity.com/paper/2410.17491/) (2024)
- [Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2306.00212/) (2023)
- [Learning Speed Adaptation for Flight in Clutter](https://mlsanity.com/paper/2403.04586/) (2024)
- [SL1-Simplex: Safe Velocity Regulation of Self-Driving Vehicles in Dynamic and Unforeseen Environments](https://mlsanity.com/paper/2008.01627/) (2020)
- [Collision Avoidance and Navigation for a Quadrotor Swarm Using End-to-end Deep Reinforcement Learning](https://mlsanity.com/paper/2309.13285/) (2023)
- [UAV-VLRR: Vision-Language Informed NMPC for Rapid Response in UAV Search and Rescue](https://mlsanity.com/paper/2503.02465/) (2025)
- [Learning a Low-dimensional Representation of a Safe Region for Safe Reinforcement Learning on Dynamical Systems](https://mlsanity.com/paper/2010.09555/) (2020)
- [N-MPC for Deep Neural Network-Based Collision Avoidance exploiting Depth Images](https://mlsanity.com/paper/2402.13038/) (2024)
- [Safe Multi-Agent Navigation guided by Goal-Conditioned Safe Reinforcement Learning](https://mlsanity.com/paper/2502.17813/) (2025)
- [Safety Guaranteed Robust Multi-Agent Reinforcement Learning with Hierarchical Control for Connected and Automated Vehicles](https://mlsanity.com/paper/2309.11057/) (2023)
- [Onboard Safety Guarantees for Racing Drones: High-speed Geofencing with Control Barrier Functions](https://mlsanity.com/paper/2201.04331/) (2022)
- [TornadoDrone: Bio-inspired DRL-based Drone Landing on 6D Platform with Wind Force Disturbances](https://mlsanity.com/paper/2406.16164/) (2024)
- [Aperiodic Communication for MPC in Autonomous Cooperative Landing](https://mlsanity.com/paper/2102.07403/) (2021)
- [Control Barrier Function Augmentation in Sampling-based Control Algorithm for Sample Efficiency](https://mlsanity.com/paper/2111.06974/) (2021)
- [Vision-driven UAV River Following: Benchmarking with Safe Reinforcement Learning](https://mlsanity.com/paper/2409.08511/) (2024)
- [SafeSwarm: Decentralized Safe RL for the Swarm of Drones Landing in Dense Crowds](https://mlsanity.com/paper/2501.07566/) (2025)
- [AgilePilot: DRL-Based Drone Agent for Real-Time Motion Planning in Dynamic Environments by Leveraging Object Detection](https://mlsanity.com/paper/2502.06725/) (2025)
- [A Safety Constrained Control Framework for UAVs in GPS Denied Environment](https://mlsanity.com/paper/1910.10826/) (2019)
- [Safe Navigation in Unmapped Environments for Robotic Systems with Input Constraints](https://mlsanity.com/paper/2410.02106/) (2024)

## Abstract (arXiv)

We introduce AttentionSwarm, a novel benchmark designed to evaluate safe and efficient swarm control in a dynamic drone racing scenario. Central to our approach is the Attention Model-Based Control Barrier Function (CBF) framework, which integrates attention mechanisms with safety-critical control theory to enable real-time collision avoidance and trajectory optimization. This framework dynamically prioritizes critical obstacles and agents in the swarm's vicinity using attention weights, while CBFs formally guarantee safety by enforcing collision-free constraints. The AttentionSwarm algorithm was developed and evaluated using a swarm of Crazyflie 2.1 micro quadrotors, which were tested indoors with the Vicon motion capture system to ensure precise localization and control. Experimental results show that our system achieves a 95-100% collision-free navigation rate in a dynamic multi-agent drone racing environment, underscoring its effectiveness and robustness in real-world scenarios. This work offers a promising foundation for safe, high-speed multi-robot applications in logistics, inspection, and racing.