TAG-K: 尾部平均化貪欲Kaczmarz法による計算効率と性能を両立したオンライン慣性パラメータ推定
TAG-K: Tail-Averaged Greedy Kaczmarz for Computationally Efficient and Performant Online Inertial Parameter Estimation
Kaczmarz法を軽量化し、貪欲な行選択と尾部平均化を組み合わせることで、ノイズに強く高速なオンライン慣性パラメータ推定を実現した。
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著者: Shuo Sha, Anupam Bhakta, Zhenyuan Jiang, Kevin Qiu, Ishaan Mahajan, Gabriel Bravo-Palacios, Brian Plancher
分類: cs.RO
原文アブストラクト
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/.
