TAG-K加速机器人惯性参数在线估计，兼顾速度与稳定性。
TAG-K: Tail-Averaged Greedy Kaczmarz for Computationally Efficient and Performant Online Inertial Parameter Estimation
- 采用贪心随机选行+尾部平均，提升收敛速度和抗噪能力。
- 在嵌入式设备上提速达20.7倍，误差降低25%。
- 适合资源受限的实时机器人控制场景。
准确的在线惯性参数估计对自适应机器人控制至关重要，可实现实时响应负载变化、环境交互和系统磨损。传统方法常难以追踪突变参数或计算开销过高，限制了其在动态环境及计算资源受限机器人系统中的应用。本文提出TAG-K，一种轻量级的Kaczmarz方法扩展，结合贪心随机行选择以实现快速收敛，以及尾部平均以增强噪声和不一致数据下的鲁棒性。该设计在保持Kaczmarz框架低每迭代复杂度的同时，实现快速稳定的参数自适应。我们在合成基准和四旋翼跟踪任务中将TAG-K与RLS、KF及其他Kaczmarz变体对比：在笔记本级CPU上求解速度提升1.5至1.9倍，在嵌入式微控制器上提升4.8至20.7倍。更重要的是，这些加速伴随更强的抗噪声能力，估计误差降低25%，端到端跟踪性能接近提升2倍。代码、文档与网站见：https://a2r-lab.org/TAG-K/。
原文摘要 · Abstract (English)
Accurate online inertial parameter estimation is essential for adaptive robotic control, enabling real-time adjustment to payload changes, environmental interactions, and system wear. Traditional methods often struggle to track abrupt parameter shifts or incur high computational costs, limiting their effectiveness in dynamic environments and for computationally constrained robotic systems. We introduce TAG-K, a lightweight extension of the Kaczmarz method that combines greedy randomized row selection for rapid convergence with tail averaging for robustness under noise and inconsistency. This design enables fast, stable parameter adaptation while retaining the low per-iteration complexity inherent to the Kaczmarz framework. We evaluate TAG-K in synthetic benchmarks and quadrotor tracking tasks against RLS, KF, and other Kaczmarz variants. TAG-K achieves 1.5x-1.9x faster solve times on laptop-class CPUs and 4.8x-20.7x faster solve times on embedded microcontrollers. More importantly, these speedups are paired with improved robustness to measurement noise and a 25% reduction in estimation error, leading to nearly 2x better end-to-end tracking performance. Website, documentation, and code available at: https://a2r-lab.org/TAG-K/.
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