Scaling humanoid robot control effectively requires coordinating learning paradigm, data diversity, and model architecture—not just increasing scale. The motion-tracking formulation and Humanoid Transformer enable structured behavioral learning that generalizes across diverse tasks.
This paper presents a scaling recipe for Behavior Foundation Models applied to humanoid robot control. The key innovation is coordinating three components: a motion-tracking learning paradigm that treats control as reproducing whole-body behaviors, strategic balance between on-policy data collection and motion diversity, and a new Humanoid Transformer architecture.