TAG-K: Tail-Averaged Greedy Kaczmarz for Computationally Efficient and Performant Online Inertial Parameter Estimation
Shuo Sha, Anupam Bhakta, Zhenyuan Jiang, Kevin Qiu, Ishaan Mahajan, Gabriel Bravo-Palacios, Brian Plancher
AI summary
Problem
Traditional online estimators like RLS and Kalman Filters struggle to adapt quickly to abrupt parameter shifts, incur high computational costs, or lack robustness to noise, hindering their deployment on dynamic, compute-limited robotic systems.
Approach
TAG-K augments the Kaczmarz algorithm with greedy row selection for rapid convergence and tail averaging to smooth measurement noise, enabling lightweight, real-time parameter updates without full matrix inversions.
Key results
- 1.5×–1.9× faster solve times on CPUs and up to 20.7× faster on microcontrollers
- 25% reduction in inertial parameter estimation error under noisy conditions
- Nearly 2× improvement in end-to-end quadrotor tracking during unknown payload changes
- Open-source framework validated on synthetic benchmarks and embedded hardware
Why it matters
Enables reliable, real-time robot adaptation on low-power embedded platforms, bridging the gap between theoretical estimation accuracy and practical robotic deployment.
Abstract
Accurate online inertial parameter estimation is essential for adaptive robotic control, enabling real-time ad- justment 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 computa- tionally 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 Kacz- marz variants. TAG-K achieves 1.5×–1.9× faster solve times on laptop-class CPUs and 4.8×–20.7× 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 2× better end-to-end tracking performance. Website, documentation, and code available at: https://a2r-lab.org/TAG-K/.