GreenWAM: Can One World-Action Model Learn Across Robot Bodies?
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…
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