CaLiSym: Learning Symplectic Dynamics of Real-World Systems through Structured Canonical Lifts
arXiv:2607.06824v1 Announce Type: cross Abstract: 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 many robotic systems of practical interest, where actuation, dissipation, and constraints continuously exchange energy and momentum with the environment. We introduce CaLiSym, a lightweight framework that extends exact symplectic learning to such systems by changing where the geometric prior is imposed. Rather than enforcing symplecticity on the meas
The continuous advancements in AI and machine learning are pushing for more robust and physics-informed models, especially for complex real-world systems like robotics.
This development offers a method to create more stable and data-efficient AI for dynamic systems, critical for applications where reliability and safety are paramount.
The ability to extend exact symplectic learning to systems with actuation and dissipation improves the foundational AI tools for robotics and other complex physical systems.
- · Robotics companies
- · AI/ML researchers
- · Autonomous systems developers
- · Hardware manufacturers for AI-driven systems
- · Developers of less robust, physics-unaware AI models
Improved simulation fidelity and control for real-world robotic systems.
Faster development and deployment of advanced autonomous systems with higher confidence in their dynamic behavior.
Acceleration of general-purpose humanoid robotics and other complex physical AI applications, reducing reliance on extensive empirical data for training.
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Read at arXiv cs.LG