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About this paper
Kevin Park, Izat Rasyad, Dvij Kalaria, Shankar Sastry
DreamVLA turns generated human motion into simulated robot demonstrations, then trains a language-conditioned policy with expert recoveries from learner states. After failure, the expert resumes from a learner state using the matching motion-plan suffix. On simulated Unitree G1 tasks, platform-picking success is 54% versus 0% for an equally sized offline control; this does not establish hardware deployment or broad generalization.
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