LawKAN: Group-Law Layers for Composition -- When Imposing a Known Law Pays Off
Overview
This research introduces LawKAN, a neural layer design that embeds known non-commutative group laws to learn local increments and correction terms for hardware deviations. The method achieves high accuracy on fixed-angle SU(2) composition with significantly fewer parameters than baselines and demonstrates transferability across multiple robot arm vendors.
Generated from attributed reports · Updated 3 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · error reduction: 27 other · Basis not reported
“crements, and a zero-initialized correction psi learns unknown deviations of the law. A pre-training criterion predicts exact representability. On fixed-angle SU(2) composition, a 54-parameter dense edge is the most accurate dense model, at 27x fewer parameters than the strongest fixed-matrix baseline, and lies 0.05 deg above the Bayes floor. On real robot arms, an isolated 306-parameter psi reaches 1.12-1.14 mm vers”
Exact source · revision 1Source owner not reported
Reported quantity · parameter count: 54 other · Basis not reported · Differing source assertions
“crements, and a zero-initialized correction psi learns unknown deviations of the law. A pre-training criterion predicts exact representability. On fixed-angle SU(2) composition, a 54-parameter dense edge is the most accurate dense model, at 27x fewer parameters than the strongest fixed-matrix baseline, and lies 0.05 deg above the Bayes floor. On real robot arms, an isolated 306-parameter psi reaches 1.12-1.14 mm vers”
Exact source · revision 1Source owner not reported
Reported quantity · parameter count: 306 other · Basis not reported · Differing source assertions
“crements, and a zero-initialized correction psi learns unknown deviations of the law. A pre-training criterion predicts exact representability. On fixed-angle SU(2) composition, a 54-parameter dense edge is the most accurate dense model, at 27x fewer parameters than the strongest fixed-matrix baseline, and lies 0.05 deg above the Bayes floor. On real robot arms, an isolated 306-parameter psi reaches 1.12-1.14 mm vers”
Exact source · revision 1Source owner not reported
Reported quantity · error range: 1.12 other · Basis not reported
“crements, and a zero-initialized correction psi learns unknown deviations of the law. A pre-training criterion predicts exact representability. On fixed-angle SU(2) composition, a 54-parameter dense edge is the most accurate dense model, at 27x fewer parameters than the strongest fixed-matrix baseline, and lies 0.05 deg above the Bayes floor. On real robot arms, an isolated 306-parameter psi reaches 1.12-1.14 mm vers”
Exact source · revision 1Source owner not reported
Reported quantity · accuracy gap: 0.05 other · Basis not reported
“crements, and a zero-initialized correction psi learns unknown deviations of the law. A pre-training criterion predicts exact representability. On fixed-angle SU(2) composition, a 54-parameter dense edge is the most accurate dense model, at 27x fewer parameters than the strongest fixed-matrix baseline, and lies 0.05 deg above the Bayes floor. On real robot arms, an isolated 306-parameter psi reaches 1.12-1.14 mm vers”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalLawKAN: Group-Law Layers for Composition -- When Imposing a Known Law Pays Off
This research introduces LawKAN, a neural layer design that embeds known non-commutative group laws to learn local increments and correction terms for hardware deviations. The method achieves high accuracy on fixed-angle SU(2) composition with significantly fewer parameters than baselines and demonstrates transferability across multiple robot arm vendors.
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