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Real-world robot failures can be autonomously converted into interactive digital twins for targeted RL, boosting out-of-distribution manipulation by 18.75% while completely bypassing manual teleoperated recovery.
Top LLMs can achieve medal-equivalent scores on elite science exams, but they falter on visual grounding and long-horizon consistency.
Real-world driving scenarios can now be seamlessly translated into controllable closed-track tests, enabling more realistic validation of automated driving systems.
