Symplectic physics-embedded learning via Lie groups Hamiltonian formulation for serial manipulator dynamics prediction.

ABSTRACT: Accurate dynamic modeling is critical for advanced robotic control, yet conventional methods struggle with manipulator nonlinear complexity. While Hamiltonian neural networks leveraging Lie group symmetries improve physical consistency of the network, existing methods overlook key limitations: unconstrained sparsity in mass, dissipation, and control matrices; redundancy in mass network outputs; and lack of validation on multi-rigid-body systems. This paper proposes a symplectic physics-embedded learning approach (SPEL) based on Lie group Hamiltonian formulations for enhanced dynamics modeling of serial manipulators. By systematically encoding physical priors such as Lie group symmetries and Hamiltonian dynamics into neural network design, SPEL enforces sparsity in mass, dissipation, and co

SUBMITTER: Wang F 

PROVIDER: S-EPMC12475180 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature