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Can a robot foundation model learn a reusable connection between body motion and the scenes that motion creates? This episode examines GreenWAM's architecture, controlled experiments, and open benchmark release to clarify what joint world-and-action learning contributes to manipulation. We cover three questions: how anatomical coordinates organize experience from different robots; why retaining extra prediction objectives improved randomized RoboTwin success; and what the LIBERO-plus result says about robustness to changed observations and task conditions. The discussion connects GreenWAM to Cosmos 3, Unified World Models, and RDT-1B, separating the inherited ideas from Sber's robotics-specific training recipe. The evaluation spans simulated manipulation benchmarks and a physical packing task on the Green humanoid. The main limitation is the absence of a clean experiment isolating transfer to a genuinely unseen body from target-specific adaptation. For researchers building generative robot controllers, the practical question is whether world prediction should remain an active training objective as the policy specializes. What changes when a controller must learn both what to do and what its actions will make happen?
