外观
论文arXiv 2609.172651 个月前
一次校准，飞行任意团队：用于协作无人机群的残差锚定低保真训练
原文链接：Calibrate Once, Fly Any Team: Residual-Grounded Low-Fidelity Training for Cooperative Drone Swarms（arXiv 2609.17265 · 发布日期：2026-09-15）
Calibrate Once, Fly Any Team: Residual-Grounded Low-Fidelity Training for Cooperative Drone Swarms
针对高保真多无人机强化学习代价高的问题，提出残差校正低保真训练，并在多项协作无人机仿真任务上验证。
核心要点
- 高保真刚体物理训练准确但计算昂贵，且随团队规模扩大碰撞处理与坠毁率上升。
- 方法在可微JAX低保真质点仿真器中优化共享去中心化策略，完全避免高保真强化学习。
- 用单机短程高保真校准飞行离线拟合每机残差集成，数据预算不随团队规模叠加。
- 在四类任务和3至18机规模中，残差校正策略均优于未校正低保真基线。
验证范围与局限：验证范围为高保真仿真中的四类协作无人机任务和3至18机团队规模。
英文摘要 (Abstract)
Training multi-agent drone-swarm policies directly in high-fidelity (HF) rigid-body physics is accurate but computationally expensive. This cost scales poorly with team size, as each additional agent multiplies contact-resolution complexity and sharply raises the in-simulation crash rate. To address this, we propose a mixed-fidelity training scheme that eliminates HF reinforcement learning entirely. A single shared, decentralized policy is optimized inside a fully-differentiable, JAX-native low-fidelity (LF) point-mass simulator. The simulator is corrected by a small, per-agent bagged residual ensemble fit once, offline, using short calibration flights in the HF simulator. Because calibration requires only one isolated drone, the data collection budget does not compound with team size. Reference trajectories are generated by rolling out an existing LF-only policy and tracked in the HF simulator by a zero-training PD controller. Evaluated across four cooperative drone tasks and team sizes from 3 to 18, the residual-corrected policy outperforms an uncorrected LF baseline in all combinations, and a from-scratch HF policy in 22 of 24 combinations tested. It trails an HF-finetuned policy by a margin that narrows steadily with team size. Ultimately, the proposed method achieves near-equivalent performance at the largest team sizes at a fraction of the computational cost, completely avoiding the high crash rates typical of HF training.
作者：Maxim Mednikov, Oren Gal
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