In a published this week on, Nvidia and Stanford University researchers propose a novel approach to transferring AI models trained in simulation to real-world autonomous machines. It uses segmentation as the interface between perception and control, leading to what the coauthors characterize as “high success” in workloads like robot grasping.

Simulators have advantages over the real world when it comes to model training in that they’re safe and almost infinitely scalable. But generalizing strategies learned in simulation to real-world machines — whether autonomous cars, robots, or drones — requires adjustment, because even the most accurate simulators can’t account for every perturbation.

Nvidia and Stanford’s technique promises to bridge the gap between simulation and real-world environments more effectively than previous approaches, namely because it decomposes

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