Sim-to-Real Transfer
The process of training a robot policy in simulation (fast, cheap, safe) and then deploying it on real hardware without retraining. The 'reality gap' — differences in physics, friction, sensor noise — causes policies to fail. Domain randomization (randomizing simulation parameters) teaches robustness. LLMs automate this process (DrEureka): they generate randomization ranges so policies transfer zero-shot to real hardware.
In practice
A robotics team building an arm for industrial picking trains thousands of policies in parallel on Isaac Sim or MuJoCo, randomly varying object mass, friction, lighting, and motor delays. The best policy is then deployed on the physical robot without further training. With DrEureka, an LLM automatically suggests randomization ranges from the task description, reducing days of manual tuning to a few hours of automated search.
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Seen in the wild
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