# BFM-Zero: Promptable Behavioral Foundation Model for Humanoid Control
**Source:** https://arxiv.org/abs/2511.04131
**Fetched:** 2026-02-13
**Type:** Research Paper

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## 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.