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# UnifoLM-WMA-0: A World-Model-Action (WMA) Framework under UnifoLM Family
<p style="font-size: 1.2em;">
<a href="https://unigen-x.github.io/unifolm-world-model-action.github.io"><strong>Project Page</strong></a> |
<a href="https://huggingface.co/collections/unitreerobotics/unifolm-wma-0-68ca23027310c0ca0f34959c"><strong>Models</strong></a> |
<a href="https://huggingface.co/unitreerobotics/datasets"><strong>Dataset</strong></a>
</p>
<div align="center">
<p align="right">
<span> 🌎English </span> | <a href="README_cn.md"> 🇨🇳中文 </a>
</p>
</div>
<div align="justify">
<b>UnifoLM-WMA-0</b> is Unitree‘s open-source world-model–action architecture spanning multiple types of robotic embodiments, designed specifically for general-purpose robot learning. Its core component is a world-model capable of understanding the physical interactions between robots and the environments. This world-model provides two key functions: (a) <b>Simulation Engine</b> – operates as an interactive simulator to generate synthetic data for robot learning; (b) <b>Policy Enhancement</b> – connects with an action head and, by predicting future interaction processes with the world-model, further optimizes decision-making performance.
</div>
## 🦾 Real-Robot Demonstrations
| <img src="assets/gifs/real_z1_stackbox.gif" style="border:none;box-shadow:none;margin:0;padding:0;" /> | <img src="assets/gifs/real_dual_stackbox.gif" style="border:none;box-shadow:none;margin:0;padding:0;" /> |
|:---:|:---:|
| <img src="assets/gifs/real_cleanup_pencils.gif" style="border:none;box-shadow:none;margin:0;padding:0;" /> | <img src="assets/gifs/real_g1_pack_camera.gif" style="border:none;box-shadow:none;margin:0;padding:0;" /> |
**Note: the top-right window shows the world model’s pretion of future action videos.**
## 🔥 News
* Sep 22, 2025: 🚀 We released the deployment code for assisting experiments with [Unitree](https://www.unitree.com/) robots.
* Sep 15, 2025: 🚀 We released the training and inference code along with the model weights of [**UnifoLM-WMA-0**](https://huggingface.co/collections/unitreerobotics/unifolm-wma-0-68ca23027310c0ca0f34959c).
## 📑 Opensource Plan
- [x] Training
- [x] Inference
- [x] Checkpoints
- [x] Deployment
## ⚙️ Installation
```
conda create -n unifolm-wma python==3.10.18
conda activate unifolm-wma
conda install pinocchio=3.2.0 -c conda-forge -y
conda install ffmpeg=7.1.1 -c conda-forge
git clone --recurse-submodules https://github.com/unitreerobotics/unifolm-world-model-action.git
# If you already downloaded the repo:
cd unifolm-world-model-action
git submodule update --init --recursive
pip install -e .
cd external/dlimp
pip install -e .
```
## 🧰 Model Checkpoints
| Model | Description | Link|
|---------|-------|------|
|$\text{UnifoLM-WMA-0}_{Base}$| Fine-tuned on [Open-X](https://robotics-transformer-x.github.io/) dataset. | [HuggingFace](https://huggingface.co/unitreerobotics/UnifoLM-WMA-0-Base)|
|$\text{UnifoLM-WMA-0}_{Dual}$| Fine-tuned on five [Unitree opensource dataset](https://huggingface.co/collections/unitreerobotics/g1-dex1-datasets-68bae98bf0a26d617f9983ab) in both decision-making and simulation modes. | [HuggingFace](https://huggingface.co/unitreerobotics/UnifoLM-WMA-0-Dual)|
## 🛢️ Dataset
In our experiments, we consider the following three opensource dataset:
| Dataset | Robot | Link |
|---------|-------|------|
|Z1_StackBox| [Unitree Z1](https://www.unitree.com/z1)|[Huggingface](https://huggingface.co/datasets/unitreerobotics/Z1_StackBox_Dataset/tree/v2.1)|
|Z1_DualArm_StackBox|[Unitree Z1](https://www.unitree.com/z1)|[Huggingface](https://huggingface.co/datasets/unitreerobotics/Z1_Dual_Dex1_StackBox_Dataset/tree/v2.1)|
|Z1_DualArm_StackBox_V2|[Unitree Z1](https://www.unitree.com/z1)|[Huggingface](https://huggingface.co/datasets/unitreerobotics/Z1_Dual_Dex1_StackBox_Dataset_V2/tree/v2.1)|
|Z1_DualArm_Cleanup_Pencils|[Unitree Z1](https://www.unitree.com/z1)|[Huggingface](https://huggingface.co/datasets/unitreerobotics/Z1_Dual_Dex1_CleanupPencils_Dataset/tree/v2.1)|
|G1_Pack_Camera|[Unitree G1](https://www.unitree.com/g1)|[Huggingface](https://huggingface.co/datasets/unitreerobotics/G1_Dex1_MountCameraRedGripper_Dataset/tree/v2.1)|
To train on your own dataset, first to have the data following the [Huggingface LeRobot V2.1](https://github.com/huggingface/lerobot) dataset format. Assume the dataset’s source directory structure is as follows:
```
source_dir/
├── dataset1_name
├── dataset2_name
├── dataset3_name
└── ...
```
Then, convert a dataset to the required format using the command below:
```python
cd prepare_data
python prepare_training_data.py \
--source_dir /path/to/your/source_dir \
--target_dir /path/to/save/the/converted/data \
--dataset_name "dataset1_name" \
--robot_name "a tag of the robot in the dataset" # e.g, Unitree Z1 Robot Arm or Unitree G1 Robot with Gripper.
```
The resulting data structure (Note: model training only supports input from the main-view camera. If the dataset includes multiple views, remove the corresponding values from the ```data_dir``` column in the CSV file.
```
target_dir/
├── videos
│ ├──dataset1_name
│ │ ├──camera_view_dir
│ │ ├── 0.mp4
│ │ ├── 1.mp4
│ │ └── ...
│ └── ...
├── transitions
│ ├── dataset1_name
│ ├── meta_data
│ ├── 0.h5
│ ├── 1.h5
│ └── ...
└── dataset1_name.csv
```
## 🚴‍♂️ Training
A. Our training strategy is outlined as follows:
- **Step 1**: Fine-tune a video generation model as the world model using the [Open-X](https://robotics-transformer-x.github.io/) dataset;
- **Step 2**: Post-train $\text{UnifoLM-WMA}$ in decision-making mode on the downstream task dataset;
<div align="left">
<img src="assets/pngs/dm_mode.png" width="600">
</div>
- **Step 3**: Post-train $\text{UnifoLM-WMA}$ in simulation mode on the downstream task dataset.
<div align="left">
<img src="assets/pngs/sim_mode.png" width="600">
</div>
**Note**: If you only require $\text{UnifoLM-WMA}$ to operate in a single mode, you may skip the corresponding step.
B. To conduct training on a single or multiple datasets, please follow the steps below:
- **Step 1**: The maximum DoF is assumed to be 16, if you have more than 16 DoF, update ```agent_state_dim``` and ```agent_action_dim``` in [configs/train/config.yaml](https://github.com/unitreerobotics/unifolm-wma/blob/working/configs/train/config.yaml) ;
- **Step 2**: Set up the input shapes for each modality in [configs/train/meta.json](https://github.com/unitreerobotics/unitree-world-model/blob/main/configs/train/meta.json);
- **Step 3**: Configure the training parameters in [configs/train/config.yaml](https://github.com/unitreerobotics/unitree-world-model/blob/main/configs/train/config.yaml). For the ```pretrained_checkpoint```, we recommend using the checkpoint " $\text{UnifoLM-WMA-0}_{Base}$ " fine-tuned on the [Open-X](https://robotics-transformer-x.github.io/) dataset;
```yaml
model:
pretrained_checkpoint: /path/to/pretrained/checkpoint;
...
decision_making_only: True # Train the world model only in decision-making mode. If False, jointly train it in both decision-making and simulation modes.
...
data:
...
train:
...
data_dir: /path/to/training/dataset/directory
dataset_and_weights: # list the name of each dataset below and make sure the summation of weights is 1.0
dataset1_name: 0.2
dataset2_name: 0.2
dataset3_name: 0.2
dataset4_name: 0.2
dataset5_name: 0.2
```
- **Step 4**: Setup ```experiment_name```, ```save_root``` variables in [scripts/train.sh](https://github.com/unitreerobotics/unitree-world-model/blob/main/scripts/train.sh);
- **Step 5**: Launch the training with the command:
```
bash scripts/train.sh
```
## 🌏 Inference under Interactive Simulation Mode
To run the world model in an interactive simulation mode, follow these steps:
- **Step 1**: (Skip this step if you just would like to test using the examples we provided) Prepare your own prompt following the format used in the [examples/world_model_interaction_prompts](https://github.com/unitreerobotics/unitree-world-model/tree/main/examples/world_model_interaction_prompts):
```
world_model_interaction_prompts/
├── images
│ ├── dataset1_name
│ │ ├── 0.png # Image prompt
│ │ └── ...
│ └── ...
├── transitions
│ ├── dataset1_name
│ │ ├── meta_data # Used for normalization
│ │ ├── 0.h # Robot state and action data; in interaction mode,
│ │ │ # only used to retrieve the robot state corresponding
│ │ │ # to the image prompt
│ │ └── ...
│ └── ...
├── dataset1_name.csv # File for loading image prompts, text instruction and corresponding robot states
└── ...
```
- **Step 2**: Specify the correct paths for ```pretrained_checkpoint```(e.g, $\text{UnifoLM-WMA-0}_{Dual}$) and ```data_dir``` in [configs/inference/world_model_interaction.yaml](https://github.com/unitreerobotics/unitree-world-model/blob/main/configs/inference/world_model_interaction.yaml)
- **Step 3**: Set the paths for ```checkpoint```, ```res_dir``` and ```prompt_dir``` in [scripts/run_world_model_interaction.sh](https://github.com/unitreerobotics/unitree-world-model/blob/main/scripts/run_world_model_interaction.sh), and specify all the dataset's name in ```datasets=(...)```. Then, launch the inference with the command:
```
bash scripts/run_world_model_interaction.sh
```
## 🧠 Inference and Deployment under Decision-Making Mode
In this setup, inference is performed on a server, while a robot client gathers observations from the real-robot and sends them to the server to query actions. The process unfolds through the following steps:
### Server Setup:
- **Step-1**: Specify ```ckpt```, ```res_dir```, ```datasets``` in [scripts/run_real_eval_server.sh](https://github.com/unitreerobotics/unifolm-world-model-action/blob/main/scripts/run_real_eval_server.sh);
- **Step-2**: Configure ```data_dir``` and ```dataset_and_weights``` in [config/inference/world_model_decision_making.yaml](https://github.com/unitreerobotics/unifolm-world-model-action/blob/f12b4782652ca00452941d851b17446e4ee7124a/configs/inference/world_model_decision_making.yaml#L225);
- **Step-3**: Launch the server:
```
conda activate unifolm-wma
cd unifolm-world-model-action
bash scripts/run_real_eval_server.sh
```
### Client Setup
- **Step-1**: Follow the instructions in [unitree_deploy/README.md](https://github.com/unitreerobotics/unifolm-world-model-action/blob/main/unitree_deploy/README.md) to create the ```unitree_deploy``` conda environment, install the required packages, launch the controllers or services on the real-robot.
- **Step-2**: Open a new terminal and establish a tunnel connection from the client to the server:
```
ssh user_name@remote_server_IP -CNg -L 8000:127.0.0.1:8000
```
- **Step-3**: Run the ```unitree_deploy/robot_client.py``` script to start inference:
```
cd unitree_deploy
python scripts/robot_client.py --robot_type "g1_dex1" --action_horizon 16 --exe_steps 16 --observation_horizon 2 --language_instruction "pack black camera into box" --output_dir ./results --control_freq 15
```
## 📝 Codebase Architecture
Here's a high-level overview of the project's code structure and core components:
```
unitree-world-model/
├── assets # Media assets such as GIFs, images, and demo videos
├── configs # Configuration files for training and inference
│ ├── inference
│ └── train
├── examples # Example inputs and prompts for running inference
├── external # External packages
├── prepare_data # Scripts for dataset preprocessing and format conversion
├── scripts # Main scripts for training, evaluation, and deployment
├── src
│ ├──unitree_worldmodel # Core Python package for the Unitree world model
│ │ ├── data # Dataset loading, transformations, and dataloaders
│ │ ├── models # Model architectures and backbone definitions
│ │ ├── modules # Custom model modules and components
│ │ └── utils # Utility functions and common helpers
└── unitree_deploy # Deployment code
```
## 🙏 Acknowledgement
Lots of code are inherited from [DynamiCrafter](https://github.com/Doubiiu/DynamiCrafter), [Diffusion Policy](https://github.com/real-stanford/diffusion_policy), [ACT](https://github.com/MarkFzp/act-plus-plus) and [HPT](https://github.com/liruiw/HPT).
## 📝 Citation
```
@misc{unifolm-wma-0,
author = {Unitree},
title = {UnifoLM-WMA-0: A World-Model-Action (WMA) Framework under UnifoLM Family},
year = {2025},
}
```