The claim matters because of where the field’s constraint sits. Generalist robot policies have been trained largely on teleoperated action data: humans physically guiding robots through tasks, hour by hour. That data is expensive and slow to collect, and it has capped how far robot foundation models can scale. DYNA-2’s bet is that the physical intuition robots need can be learned from video of humans doing things, then transferred to robot hardware. “By building a World-Action Model that…