Dynamic robotic cloth folding with efficient Koopman operator-based model predictive control
The method integrates physics simulation with data-driven modeling and Model Predictive Control (MPC) to fold cloth quickly and accurately on a robot. By using Koopman operator regression, nonlinear cloth dynamics are recast into a linear form, allowing an MPC strategy to generate fast trajectories that successfully transfer from simulation to real-world robotic execution.