How Robot Learning Closes The Simulation Gap

Synthetic data and domain randomization are reportedly cutting robot deployment time ~40%, but introduce new blind-spot and model-collapse risks.

• Domain randomization cuts deployment time: Training robots on simulations with randomized textures, lighting, camera angles, and object mass has reduced deployment time for some new robotic arms by nearly 40% versus last year.
• Sim-to-real gap persists: Physics engines never perfectly match reality — small differences in friction, weight distribution, and sensor noise cause robots that work in simulation to fail on real objects.

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