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Building Worlds That Train Robots | World Labs

worldlabs.ai · 2,352 words · saved by 1 readers

Real-to-sim-to-real (R2S2R) as a scalable engine for training and evaluating robot policies

When spatial intelligence becomes physical, the north star goal is to advance the field of robotics. Many laboratory demonstrations of robots today show promising progress, but the real challenge is making them work reliably in the real world, where objects shift, clutter accumulates, lighting changes, and physical interactions vary from one trial to the next. This gap between a compelling demo and reliable operation is a major barrier to putting robotic systems to work at scale. Preparing robots for the real world requires extensive data collection and repeated testing on physical…

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