Learning to Throw: Agile and Accurate Cable-Suspended Payload Delivery with a Quadrotor

• URL: http://arxiv.org/abs/2606.27603v1
• Date: Thu, 25 Jun 2026 23:30:52 GMT
• Title: Learning to Throw: Agile and Accurate Cable-Suspended Payload Delivery with a Quadrotor
• Abstract summary: Quadrotors offer the agility needed to rapidly transport suspended payloads during time-critical applications.<n>State-of-the-art approaches typically rely on model-based trajectory optimization and tracking.<n>We propose a hybrid simulation framework that couples a high-fidelity analytical quadrotor model with a physics solver for complex rope and payload interactions.
• Score: 17.676294984292433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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