Not all robots are created equal — but what if you could design one for a specific task? We propose Transformer Transformer, a unified model that does exactly this: hand it a manipulation demonstration, and it generates a complete robot — every link, joint, motor, and inertial property — optimized for that motion. We fabricated one such design for cloth flinging on an ALOHA² bimanual platform; it reduced tracking error by 73% and max joint speed by 30% versus the original.

Behind that result is a diffusion transformer trained on RoboTokens, a unified tokenization of robot embodiments, states, and actions. The same architecture spans embodiment spaces (wheeled bimanual, quadrupeds, humanoids) and use cases (embodiment generation, cross-embodiment control). Rather than overfitting to one reward function, it is a dynamics model whose reward-agnostic predictions are converted into reward-specific value predictions at inference time, then used to steer embodiment diffusion through a procedure we call Dynamics Self-Guidance. Experiments across three design spaces show zero-shot optimization of unseen rewards and trajectories, improving performance and runtime over an evolutionary baseline.