Domain randomization and generative models for robotic grasping

Researchers trained a grasp-planning model on millions of procedurally generated objects using domain randomization.

The autoregressive deep model performed well in both simulation and real-world tests on objects it had never seen before.

The results suggest large-scale synthetic variation can improve robotic grasp generalization without relying on huge labeled datasets.