Sim2Real

The gap between simulation and reality: simulation doesn’t accurately reproduce physics, visuals, and hardware characteristics. Domain randomization: widely and randomly vary parameters (friction, masses, lighting, delays, noise) forcing the policy to learn a robust representation – the real world becomes “one sample” from the distribution. Two engineering points: calibration of randomization range based on empirical data from the real environment (not randomly); visual alignment (camera calibration, replacing real background in rendering). Successes: Dactyl (Rubik’s Cube), ANYmal (rough terrain). RGB-based capture without examples: alignment + visual randomization + physics – all three are necessary.

Related: [VLA Models], [Simulation Environment], [Data and Environment are More Important than Algorithm]