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Generating robot training data that bridges the sim2real gap doesn't require painstakingly detailed simulation environments; instead, a neural simulator can transform classical simulations into realistic representations using only a small amount of real-world data.
Robots can now adapt to unforeseen errors and dynamically replan trajectories in real-time by simply incorporating sparse, human-provided or planner-provided "referring points" into their visuomotor policies.
