# Safe and Scalable Multi-Drone Payload Transport via CBF-based Reinforcement Learning with Zero-Shot Sim-to-Real Transfer

arXiv: https://arxiv.org/abs/2607.20665 · HTML: https://mlsanity.com/paper/2607.20665/

## 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 16 of its 45 references that are in the map.
- First posted on arXiv 22 Jul 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)

- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation](https://mlsanity.com/paper/2211.02127/) (2022)
- [Multi-Agent Constrained Policy Optimisation](https://mlsanity.com/paper/2110.02793/) (2021)
- [GCBF+: A Neural Graph Control Barrier Function Framework for Distributed Safe Multi-Agent Control](https://mlsanity.com/paper/2401.14554/) (2024)
- [Control Barrier Functions and Input-to-State Safety with Application to Automated Vehicles](https://mlsanity.com/paper/2206.03568/) (2022)
- [DRAL: Deep Reinforcement Adaptive Learning for Multi-UAVs Navigation in Unknown Indoor Environment](https://mlsanity.com/paper/2409.03930/) (2024)
- [Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal Control](https://mlsanity.com/paper/2502.03640/) (2025)
- [Human-Aware Physical Human-Robot Collaborative Transportation and Manipulation with Multiple Aerial Robots](https://mlsanity.com/paper/2210.05894/) (2022)
- [Agile and Cooperative Aerial Manipulation of a Cable-Suspended Load](https://mlsanity.com/paper/2501.18802/) (2025)
- [Solving Multi-Agent Safe Optimal Control with Distributed Epigraph Form MARL](https://mlsanity.com/paper/2504.15425/) (2025)
- [Nonlinear Model Predictive Control for Cooperative Transportation and Manipulation of Cable Suspended Payloads with Multiple Quadrotors](https://mlsanity.com/paper/2303.06165/) (2023)
- [Decentralized Aerial Manipulation of a Cable-Suspended Load using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2508.01522/) (2025)
- [Nonlinear MPC for Full-Pose Manipulation of a Cable-Suspended Load using Multiple UAVs](https://mlsanity.com/paper/2301.08545/) (2023)
- [HPA-MPC: Hybrid Perception-Aware Nonlinear Model Predictive Control for Quadrotors with Suspended Loads](https://mlsanity.com/paper/2411.11982/) (2024)
- [Cooperative Transportation using Multiple Single-Rotor Robots and Decentralized Control for Unknown Payloads](https://mlsanity.com/paper/2111.01963/) (2021)
- [Optimal Trajectory Planning for Cooperative Manipulation with Multiple Quadrotors Using Control Barrier Functions](https://mlsanity.com/paper/2503.01096/) (2025)

## Abstract (arXiv)

Multi-drone payload transportation has emerged as a promising research paradigm with potential applications in construction, logistics, and disaster response. However, the complex coupled dynamics among drones, cables, and payloads pose significant challenges, and existing approaches remain limited in safety and scalability, particularly in dynamic and unstructured environments. In this work, we propose a learning-based framework for safe and scalable multi-drone cooperative payload transport. We introduce a minimal 2D abstraction that preserves the task-relevant drone-payload coupling required for coordination and safety, while remaining computationally efficient for large-scale learning. Using domain randomization over team size and physical parameters, we train a fully distributed policy via Discrete Graph Control Barrier Function Proximal Policy Optimization (DGPPO), enabling robust zero-shot sim-to-real transfer without fine-tuning. Extensive real-world evaluations demonstrate that a single learned policy generalizes across varying team sizes and task scenarios. Furthermore, multi-group hardware experiments show that the same policy can safely operate in dynamic environments, where other drone teams act as moving obstacles. These results indicate that the proposed framework enables efficient, safe, and scalable multi-drone payload transportation with strong generalization to complex real-world conditions.