A Unified Model for Motion-Conditioned Robot Co-design

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.