From Simulation-to-Reality for Robotics

This post introduces domain randomization as a technique to bridge the sim-to-real gap in robotics. It details how randomizing parameters like gravity, floor friction, motor power, and sensor noise during training helps AI models generalize to real-world conditions. Additionally, it highlights the importance of tracking non-reward metrics such as action smoothness, average torque, and peak torque to detect hidden failure modes like motor overheating and mechanical wear, which are crucial for robust robotic systems.