How Robots Learn to Grasp From Simulation and Demonstration

Two complementary data sources feed most modern grasping systems.

Modern grasping systems are typically trained on a mix of simulated data (millions of randomly generated objects and grasp attempts, scored automatically by a physics engine) and real-world demonstration data (a smaller but higher-fidelity set of human-teleoperated or human-labeled grasps).

Why simulation alone isn't enough

Simulated grasp success doesn't perfectly predict real-world success, since simulated friction, contact, and object properties are always simplifications — a policy trained purely in simulation typically needs some real-world fine-tuning or validation before it's trusted in deployment.

Why demonstration data alone isn't enough either

Real-world demonstration data is expensive and slow to collect at scale compared to simulation, which can generate essentially unlimited synthetic grasp attempts. Most practical systems today lean on simulation for scale and real demonstration data for accuracy and edge cases simulation misses.