CaLiSym: Learning Symplectic Dynamics of Real-World Systems through Structured Canonical Lifts
Physics-informed learning promises data-efficient and stable dynamics prediction, yet its strongest geometric guarantees have largely remained confined to closed conservative systems. This excludes robotic systems of interest, where actuation, dissipation, and constraints exchange energy and momentum with the environment. We introduce CaLiSym, a lightweight framework that extends symplectic learning to such systems by changing where the geometric prior is imposed. Rather than enforcing symplecticity on the measured state, CaLiSym embeds the state and its ports into a lifted phase space, where the dynamics evolve through a symplectic map. The lift is explicit and algebraic, requiring neith...
| Authors | Aristotelis Papatheodorou, Pranav Vaidhyanathan, Natalia Ares et al. |
|---|---|
| Primary category | cs.RO |
| Published | 7 July 2026 |
| Last updated at source | 15 July 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.