Humanoid Locomotion as Next Token Prediction
Ilija Radosavovic Bike Zhang Baifeng Shi Jathushan Rajasegaran Sarthak Kamat Trevor Darrell Koushil Sreenath Jitendra Malik
We cast real-world humanoid control as a next token prediction problem, akin
to predicting the next word in language. Our model is a causal transformer
trained via autoregressive prediction of sensorimotor trajectories. To account
for the multi-modal nature of the data, we perform prediction in a
modality-aligned way, and for each input token predict the next token from the
same modality. This general formulation enables us to leverage data with
missing modalities, like video trajectories without actions. We train our model
on a collection of simulated trajectories coming from prior neural network
policies, model-based controllers, motion capture data, and YouTube videos of
humans. We show that our model enables a full-sized humanoid to walk in San
Francisco zero-shot. Our model can transfer to the real world even when trained
on only 27 hours of walking data, and can generalize to commands not seen
during training like walking backward. These findings suggest a promising path
toward learning challenging real-world control tasks by generative modeling of
sensorimotor trajectories.