Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks
In many real-world systems, including articulated robots and biomechanical models, rotations are defined in joint space and naturally parameterized by Euler angles with bounded ranges. Yet regressing Euler angles remains challenging, as their discontinuities and singularities often destabilize training. In this work, we revisit Euler-angle regression and show that its effectiveness depends critically on the interaction between rotation representation, regression architecture, and domain constraints. We introduce a new framework that combines range-aware Euler modeling with Kolmogorov-Arnold Ne
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- PossiblePossibly related (embedding) · 49%modelplaneai/modelplane →
- LinkedLinked via arxiv author · 85%Yangting Sun →
“Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks”
- LinkedLinked via arxiv author · 85%Zijun Cui →
“Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks”
- LinkedLinked via arxiv author · 85%Yufei Zhang →
“Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks”
- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
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