[CS.AI] Scaling Behavior Foundation Models for Humanoid Robots

Abstract

Humanoid control requires natural whole-body coordination, precise real-time responses to control signals, and robust generalization across diverse environmental contexts, making it a cornerstone for generalist embodied agents. Behavior Foundation Models (BFMs) have recently emerged as a promising solution to address these challenges by leveraging large-scale behavioral data to achieve superior expressiveness, versatility, and generalization. However, despite growing interest in scaling BFMs to further improve their capabilities, it remains unclear how key factors, including the learning paradigm, behavioral data, and model architecture, should be coordinated to enable effective scaling.

In this work, we revisit the scaling recipe for BFMs and demonstrate that substantial performance gains can be achieved through the coordination of three core components:

1. the learning paradigm of motion tracking that reformulates diverse humanoid control problems as the reproduction of integrated whole-body behaviors in the global frame;
2. the strategic synergy between on-policy rollout quantity and reference motion diversity; and
3. the expressive and scalable model architecture termed Humanoid Transformer that facilitates the natural emergence of structured behavioral representations.

Through extensive experiments in both simulation and real-world deployment, we demonstrate that our approach yields significant improvements in control fidelity and task generalization, reducing Mean Per-Keypoint Position Error (MPKPE) on the test set by over 10% in local mode and 82% in global mode compared with existing humanoid controllers. These results establish BFM as a principled and effective foundation for scalable and general-purpose humanoid control.

Blogger's Review: This paper provides an in-depth exploration of BFMs, illustrating how the coordination of key factors can enhance humanoid robot control performance, particularly in complex environments. The findings offer crucial theoretical and practical guidance for future developments in intelligent robotics.