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Scaling up robot data and closing the loop with state decoding and automated reward scoring allows a 2B parameter video world simulator to outperform larger, dedicated robotic world models in real-world policy transfer.
Decomposing robotic manipulation into coarse and fine-grained actions isn't just conceptually cleaner—it actually unlocks a sweet spot where learning difficulty is balanced, boosting performance.
Forget brittle multi-policy execution and manual resets: RoboClaw's "Entangled Action Pairs" let robots self-correct and learn continuously, slashing human intervention by over 50% while boosting task success.
Forget sub-task prediction – the secret to better robot policies is reasoning directly in the action space with a sequence of coarse action intents.
