Sim-to-Real Transfer Learning

A machine learning approach that generates large-scale training data in low-cost, controlled simulation environments (games, digital twins) to learn universal physics and control principles, then fine-tunes with minimal real-world data to enable AI systems to perform complex tasks in reality. This is a key lever for breaking through the expensive real-world data bottleneck.

Read the daily articles behind this idea on the Chinese edition.