用条件扩散模型学习无人机机械臂的动态残差，提升复杂工况下的控制精度。
AERMANI-Diffusion: Regime-Conditioned Diffusion for Dynamics Learning in Aerial Manipulators
- 基于条件扩散过程与轻量时序编码器建模动态残差分布
- 在真实测试中显著提升轨迹跟踪精度，支持突变工况和未知负载
- 适合需要高精度动态建模的空中机械臂系统
无人机机械臂在飞行中会因构型变化产生快速、非线性的惯性耦合力和气动力，导致精确动力学建模成为可靠控制的核心挑战。解析模型在这些非线性、非平稳效应下精度下降，而标准数据驱动方法如深度神经网络和高斯过程无法刻画不同工作状态下出现的多样残差行为。本文提出一种分段条件扩散框架，利用条件扩散过程和轻量时序编码器对残差力的完整分布进行建模。该编码器提取近期运动与构型的紧凑表征，使模型在突发状态转换或未知载荷情况下仍能保持稳定的残差预测。结合自适应控制器后，该框架可实现动力学不确定性补偿，并在真实实验中显著提升轨迹跟踪精度。
原文摘要 · Abstract (English)
Aerial manipulators undergo rapid, configuration-dependent changes in inertial coupling forces and aerodynamic forces, making accurate dynamics modeling a core challenge for reliable control. Analytical models lose fidelity under these nonlinear and nonstationary effects, while standard data-driven methods such as deep neural networks and Gaussian processes cannot represent the diverse residual behaviors that arise across different operating conditions. We propose a regime-conditioned diffusion framework that models the full distribution of residual forces using a conditional diffusion process and a lightweight temporal encoder. The encoder extracts a compact summary of recent motion and configuration, enabling consistent residual predictions even through abrupt transitions or unseen payloads. When combined with an adaptive controller, the framework enables dynamics uncertainty compensation and yields markedly improved tracking accuracy in real-world tests.
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