Behavior Foundation Model for Humanoid Robots

2025-09-17

AI Summary

Existing whole-body control (WBC) frameworks suffer from strong task specificity, reliance on hand-crafted reward functions, and poor generalization—hindering adaptation to complex real-world scenarios and diverse control modalities. To address this, we propose the Behavior Foundation Model (BFM), a unified WBC framework for humanoid robots, pretrained on large-scale heterogeneous behavioral data. BFM employs a conditional variational autoencoder (CVAE) to model the distribution of human-robot behaviors and introduces masked online distillation to efficiently extract reusable behavioral knowledge. This enables zero-shot transfer and rapid adaptation to novel skills without retraining. Evaluated in both simulation and on physical humanoid platforms, BFM demonstrates substantial improvements in cross-task generalization, multimodal control compatibility, and environmental adaptability. Our approach establishes a new paradigm for generalist embodied intelligence in robot control.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Whole-body control (WBC) of humanoid robots has witnessed remarkable progress in skill versatility, enabling a wide range of applications such as locomotion, teleoperation, and motion tracking. Despite these achievements, existing WBC frameworks remain largely task-specific, relying heavily on labor-intensive reward engineering and demonstrating limited generalization across tasks and skills. These limitations hinder their response to arbitrary control modes and restrict their deployment in complex, real-world scenarios. To address these challenges, we revisit existing WBC systems and identify a shared objective across diverse tasks: the generation of appropriate behaviors that guide the robot toward desired goal states. Building on this insight, we propose the Behavior Foundation Model (BFM), a generative model pretrained on large-scale behavioral datasets to capture broad, reusable behavioral knowledge for humanoid robots. BFM integrates a masked online distillation framework with a Conditional Variational Autoencoder (CVAE) to model behavioral distributions, thereby enabling flexible operation across diverse control modes and efficient acquisition of novel behaviors without retraining from scratch. Extensive experiments in both simulation and on a physical humanoid platform demonstrate that BFM generalizes robustly across diverse WBC tasks while rapidly adapting to new behaviors. These results establish BFM as a promising step toward a foundation model for general-purpose humanoid control.

Problem

• Addressing task-specific limitations in humanoid robot whole-body control
• Overcoming limited generalization across diverse tasks and skills
• Enabling flexible operation and rapid adaptation to new behaviors

Innovation

• Generative model pretrained on behavioral datasets
• Masked online distillation with CVAE framework
• Enables flexible operation across control modes