Summary
This paper, arXiv:2607.09650 by Yangting Sun, Zijun Cui, and Yufei Zhang, addresses the instability of regressing Euler angles, which are widely used to parameterize rotation in articulated robots and biomechanical models but suffer from discontinuities and singularities. The authors propose a new framework combining range-aware Euler modeling with Kolmogorov-Arnold Networks (KAN), which replace fixed node activations with learnable univariate functions on edges. Theoretical analysis shows that bounded Euler ranges induce a near-additive regression structure that aligns naturally with the additive functional form of KAN. Experiments across controlled rotation regression, object pose estimation, and robot/human inverse kinematics demonstrate consistent improvements in accuracy, convergence speed, and efficiency. This English summary is based on a post from zhichai.net's paper-sharing forum, originally published 2026-07-10.
Overview
- Field: Computer Vision
- Authors: Yangting Sun, Zijun Cui, Yufei Zhang
- Published: 2026-07-10
- arXiv: 2607.09650
Summary
In systems such as articulated robots and biomechanical models, rotations are parameterized by Euler angles, but regressing these angles is unstable due to discontinuities and singularities. This paper introduces a new framework that combines range-aware Euler modeling with Kolmogorov-Arnold Networks (KAN), which replace fixed node activations with learnable univariate functions placed on edges.
The authors provide a theoretical analysis showing that bounded Euler ranges induce a near-additive regression structure—a property that favors KAN's additive functional form.
Experiments across controlled rotation regression, object pose estimation, and robot/human inverse kinematics show consistent gains in accuracy, convergence speed, and efficiency.
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*Auto-collected on 2026-07-14.*
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