Ada-BFM: Adapting Behavior Foundation Models for Humanoid Scene Interaction

Behavior Foundation Models (BFMs) have emerged as a promising paradigm for general-purpose humanoid control through pretraining on large-scale behavioral data. However, their proficiency in whole-body coordination does not readily extend to scene interaction, which requires adapting motion to scene-dependent geometric constraints and contact dynamics. To bridge this gap, we introduce Ada-BFM, a post-training framework that adapts pretrained BFMs for humanoid scene interaction. Ada-BFM augments the original architecture with a lightweight adapter, enabling interaction-specific adjustments while leveraging pretrained control capabilities. Rather than producing a separate controller for each task, the framework yields a shared model for each interaction domain, supporting diverse tasks involving terrain or object interaction. Extensive experiments in simulation and the real world demonstrate that Ada-BFM enables diverse scene interaction behaviors, accelerates learning compared with training from scratch, and largely preserves pretrained behavioral competence. These findings establish Ada-BFM as an effective and efficient approach to extending the capabilities of BFMs beyond their pretraining settings while retaining their strengths in general whole-body control.