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BFM-Zero: Promptable Behavioral Foundation Model for Humanoid Control
Source: https://arxiv.org/abs/2511.04131 Fetched: 2026-02-13 Type: Research Paper
Paper Information
- arXiv ID: 2511.04131
- Authors: Yitang Li, Zhengyi Luo, Tonghe Zhang, Cunxi Dai, and nine additional collaborators
- Submission Date: November 6, 2025
- Field: Computer Science - Robotics
Abstract
BFM-Zero is a framework for training humanoid robots using unsupervised reinforcement learning. The system enables a single policy to handle multiple tasks through a shared latent representation without requiring retraining.
Core Methodology
Latent Space Architecture
The framework "learns an effective shared latent representation that embeds motions, goals, and rewards into a common space." This unified representation enables versatile control approaches.
Technical Foundations
Rather than traditional on-policy RL methods, BFM-Zero builds on unsupervised reinforcement learning and Forward-Backward models, providing what the authors describe as an "objective-centric, explainable, and smooth latent representation of whole-body motions."
Sim-to-Real Transfer
Critical components include reward shaping, domain randomization, and history-dependent asymmetric learning to bridge simulation-reality gaps.
Real-World Implementation on Unitree G1
Hardware Platform
Experiments deployed on a Unitree G1 humanoid robot.
Inference Capabilities
The promptable policy supports multiple downstream applications: zero-shot motion tracking, goal-directed reaching, reward optimization, and few-shot adaptation without retraining.
Significance
BFM-Zero provides a unified foundation model approach to humanoid control, enabling versatile task execution through promptable interfaces rather than task-specific training.