[2606.27603] Learning to Throw: Agile and Accurate Cable-Suspended Payload Delivery with a Quadrotor
[Submitted on 25 Jun 2026]
Title:Learning to Throw: Agile and Accurate Cable-Suspended Payload Delivery with a Quadrotor
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Abstract:Quadrotors offer the agility needed to rapidly transport suspended payloads during time-critical applications, including search-and-rescue and medical delivery. While suspended-payload transport and traversal for these missions are well studied, the highly dynamic targeted release of the payload remains comparatively underexplored. State-of-the-art approaches typically rely on model-based trajectory optimization and tracking; however, these methods often yield sub-optimal performance due to conservative feasibility constraints, tracking errors, and the inherent difficulty of analytically modeling flexible rope dynamics. To overcome these limitations, we propose a hybrid simulation framework that couples a high-fidelity analytical quadrotor model with a physics solver for complex rope and payload interactions. By exchanging forces between the two domains at every step, we obtain a physically accurate simulation of the suspended-payload system. Leveraging this environment, we train a deep reinforcement learning (RL) policy that executes agile, accurate payload throws to designated targets. Deployed zero-shot on hardware, our RL policy pushes the boundary of the agility-accuracy trade-off, outperforming the model-based baseline by reducing the landing error by up to 50% and the throw duration by up to 30%. Ablation studies confirm that the coupled simulation is the key enabler of these gains. We further show that the same pipeline trains a policy driven by visual observations rather than an explicit state estimate, achieving accuracy comparable to that of the state-based policy. To accelerate future research in dynamic aerial manipulation, we open-source the simulator to the community upon acceptance.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.27603 [cs.RO]
(or arXiv:2606.27603v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.27603
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yifan Zhai [view email] [v1] Thu, 25 Jun 2026 23:30:52 UTC (17,308 KB)
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