[Submitted on 22 Jul 2026]
Title:Safe and Scalable Multi-Drone Payload Transport via CBF-based Reinforcement Learning with Zero-Shot Sim-to-Real Transfer
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Abstract: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.
Comments: Published in IEEE Robotics and Automation Letters (Early Access), 2026
Subjects:
Robotics (cs.RO); Multiagent Systems (cs.MA); Systems and Control (eess.SY)
Cite as: arXiv:2607.20665 [cs.RO]
(or arXiv:2607.20665v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.20665
arXiv-issued DOI via DataCite (pending registration)
Related DOI:
https://doi.org/10.1109/LRA.2026.3715346
DOI(s) linking to related resources
Submission history
From: Jaeyoun Choi [view email] [v1] Wed, 22 Jul 2026 19:01:57 UTC (6,242 KB)
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