Training reinforcement learning policies across thousands of randomized simulation environments to guarantee zero-shot real-world transfer.
During training, algorithms randomize link masses by ±20%, joint friction by ±40%, and sensor latencies by 5 to 35 milliseconds, forcing neural policies to develop robust feedback control.
Randomizing floor textures, ambient illumination, and shadow angles prevents perception networks from overfitting to simulation graphics, enabling reliable real-world object recognition.