Roboparty / P-BFM

P-BFM: Perceptive Behavioral Foundation Models for Humanoid Control via Unsupervised Reinforcement Learning
 English | 中文 
P-BFM is a perceptive behavioral foundation model for terrain-adaptive humanoid control. It maps **motion references, target body states, and reward functions** into a shared intent space. A single frozen policy combines these intents with depth perception and explicit heading and velocity instructions to execute whole-body behaviors across terrains.
Training uses **40 flat-ground LaFAN motion sequences and online interaction with simulated terrains**, without terrain-specific motion demonstrations. The policy transfers zero-shot to a **Unitree G1 with onboard depth sensing**, supporting motion tracking, goal reaching, reward inference, teleoperation, and terrain-aware recovery.
Architecture · Training · Inference / export
## Layout
 [code] 
Configuration files are bundled under `pbfm/configs/`, including in
the installed package; downloaded motions, weights, replay and videos stay external.
## Model and settings
| Component | Hidden width × layers |
| --- | --- |
| Actor / Forward F | 2048 × 6 |
| QD | 1024 × 4 |
| QAux | 2048 × 4 |
| QH / QV | 1024 × 3 |
The four critics are independent; each uses two Q estimates. The training preset
is `fb_depth`: BCE/log-odds, D=.01, Aux=.02, QH=QV=.002, dynamic Aux normalization,
LaFAN-only, slippage=-4, stumble=-2. Depth is Crop18 (`8×18×32`), CNN stride 1,
frame feature 256 and GRU feature 256. The velocity estimator is depth-supervised.
## Install and train
Python 3.10, MJLab 1.4, MuJoCo 3.8, PyTorch 2.7. Install from the repository root:
 [code] 
Fresh 192M training (eight free GPUs, GPU replay; edit the data/output paths):
 [code] 
Add `--use-wandb` after authenticating separately. Add `--smoke` with a different
new work directory for a 32768-step short test. These commands do not stop other
users' jobs or wait for free GPUs.
Use the scratch entry point above for training from step 0: it sets 192020480
total global steps and starts the terrain curriculum at step 0.
See Training for schedule and checkpoint requirements.
## Inference and export
 [code] 
Tracking selects terrain and camera settings in the same entry point. The policy ONNX includes depth
encoding and velocity estimation; the backward encoder is exported separately.
Input normalization, context and hardware control requirements are described in
the inference guide.
## Tests
 [code] 
External-data/hardware checks are explicitly skipped when their inputs are
unavailable. CPU and small-model ONNX tests are not full training or real-robot
validation.
PBFM builds on UFO. Original notices and the
upstream CC BY-NC 4.0 license are retained.
## Project and community
- Project website and demos: https://roboparty.github.io/P-BFM/
- Paper: https://arxiv.org/abs/2610.15833
- Team: RoboPartyLab
- Contact: xuewangusst@gmail.com
- To join our discussion group, add **15251239451** on WeChat.
## Repository branches
- `main`: PBFM traini