Control

Dynamics Model

Dynamics Model is a mathematical or learned representation of how a system's state evolves under applied inputs. Analytic robot dynamics take the manipulator equation form derived from Lagrangian mechanics, with mass matrix, Coriolis, and gravity terms, while learned dynamics models are neural networks trained on transition data, as in model-based reinforcement learning and world models. Accuracy over long prediction horizons, especially through contact events, is the central difficulty.

Why it matters for physical AI

A usable dynamics model turns control into planning: model predictive control, trajectory optimization, and model-based RL all trade real-world trial and error for computation against the model, a decisive advantage when hardware time is scarce.