188 lines
11 KiB
Markdown
188 lines
11 KiB
Markdown
# UnifoLM-WMA-0: A World-Model-Action (WMA) Framework under UnifoLM Family
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<p style="font-size: 1.2em;">
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<a href="https://unigen-x.github.io/unifolm-world-model-action.github.io"><strong>Project Page</strong></a> |
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<a href="https://huggingface.co/unitreerobotics/UnifoLM-WMA-0"><strong>Models</strong></a> |
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<a href="https://huggingface.co/unitreerobotics/datasets"><strong>Dataset</strong></a>
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</p>
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<div align="center">
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<p align="right">
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<span> 🌎English </span> | <a href="README_cn.md"> 🇨🇳中文 </a>
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</p>
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</div>
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<div align="justify">
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<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.
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</div>
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## 🦾 Real Robot Deployment
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| <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;" /> |
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|:---:|:---:|
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| <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;" /> |
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**Note: the top-right window shows the world model’s prediction of future environmental changes.**
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## 📑 Opensource Plan
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- [x] Training
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- [x] Inference
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- [x] Checkpoints
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- [ ] Deployment
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## ⚙️ Installation
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```
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conda create -n unifolm-wma python==3.10.14
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conda activate unifolma
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conda install pinocchio=3.2.0 -c conda-forge -y
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conda install ffmpeg=7.1.1 -c conda-forge
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git clone --recurse-submodules https://github.com/unitreerobotics/unifolm-world-model-action.git
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# If you already downloaded the repo:
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cd unifolm-world-model-action
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git submodule update --init --recursive
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pip install -e .
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cd external/dlimp
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pip install -e .
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```
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## 🧰 Model Checkpoints
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| Model | Description | Link|
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|---------|-------|------|
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|$\text{UnifoLM-WMA-0}_{Base}$| Fintuned on [Open-X](https://robotics-transformer-x.github.io/) dataset. | [HuggingFace](https://huggingface.co/unitreerobotics/UnifoLM-WMA-0)|
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|$\text{UnifoLM-WMA-0}_{Dual}$| Fintuned 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)|
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## 🛢️ Dataset
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In our experiments, we consider the following three opensource dataset:
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| Dataset | Robot | Link |
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|---------|-------|------|
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|Z1_StackBox| [Unitree Z1](https://www.unitree.com/z1)|[Huggingface](https://huggingface.co/datasets/unitreerobotics/Z1_StackBox_Dataset)|
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|Z1_DualArm_StackBox|[Unitree Z1](https://www.unitree.com/z1)|[Huggingface](https://huggingface.co/datasets/unitreerobotics/Z1_DualArmStackBox_Dataset)|
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|Z1_DualArm_StackBox_V2|[Unitree Z1](https://www.unitree.com/z1)|[Huggingface](https://huggingface.co/datasets/unitreerobotics/Z1_DualArm_StackBox_Dataset_V2)|
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|Z1_DualArm_Cleanup_Pencils|[Unitree Z1](https://www.unitree.com/z1)|[Huggingface](https://huggingface.co/datasets/unitreerobotics/Z1_DualArm_CleanupPencils_Dataset)|
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|G1_Pack_Camera|[Unitree G1](https://www.unitree.com/g1)|[Huggingface](https://huggingface.co/datasets/unitreerobotics/G1_MountCameraRedGripper_Dataset)
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To train on your own dataset, first to have the data following the [Huggingface LeRobot](https://github.com/huggingface/lerobot) dataset format. Assume the dataset’s source directory structure is as follows:
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```
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source_dir/
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├── dataset1_name
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├── dataset2_name
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├── dataset3_name
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└── ...
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```
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Then, convert a dataset to the required format using the command below:
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```python
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cd prepare_data
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python prepare_training_data.py \
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--source_dir /path/to/your/source_dir \
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--target_dir /path/to/save/the/converted/data \
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--dataset_name "dataset1_name" \
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--robot_name "a tag of the robot in the dataset" # e.g, Unitree Z1 Robot Arm or Unitree G1 Robot with Gripper.
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```
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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.
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```
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target_dir/
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├── videos
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│ ├──dataset1_name
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│ │ ├──camera_view_dir
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│ │ ├── 0.mp4
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│ │ ├── 1.mp4
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│ │ └── ...
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│ └── ...
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├── transitions
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│ ├── dataset1_name
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│ ├── meta_data
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│ ├── 0.h5
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│ ├── 1.h5
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│ └── ...
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└── dataset1_name.csv
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```
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## 🚴♂️ Training
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A. Our training strategy is outlined as follows:
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- **Step 1**: Fine-tune a video generation model as the world model using the [Open-X](https://robotics-transformer-x.github.io/) dataset;
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- **Step 2**: Post-train $\text{UnifoLM-WMA}$ in decision-making mode on the downstream task dataset;
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<div align="left">
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<img src="assets/pngs/dm_mode.png" width="600">
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</div>
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- **Step 3**: Post-train $\text{UnifoLM-WMA}$ in simulation mode on the downstream task dataset.
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<div align="left">
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<img src="assets/pngs/sim_mode.png" width="600">
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</div>
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**Note**: If you only require $\text{UnifoLM-WMA}$ to operate in a single mode, you may skip the corresponding step.
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B. To conduct training on a single or multiple datasets, please follow the steps below:
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- **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) ;
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- **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);
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- **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;
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```yaml
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model:
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pretrained_checkpoint: /path/to/pretrained/checkpoint;
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...
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dicision_making_only: True # Train the world model only in decision-making mode. If False, jointly train it in both decision-making and simulation modes.
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...
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data:
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...
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train:
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...
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data_dir: /path/to/training/dataset/directory
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dataset_and_weights: # list the name of each dataset below and make sure the summation of weights is 1.0
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dataset1_name: 0.2
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dataset2_name: 0.2
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dataset3_name: 0.2
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dataset4_name: 0.2
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dataset5_name: 0.2
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```
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- **Step 4**: Setup ```experiment_name```, ```save_root``` variables in [scripts/train.sh](https://github.com/unitreerobotics/unitree-world-model/blob/main/scripts/train.sh);
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- **Step 5**: Lanuch the training with the command:
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```
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bash scripts/train.sh
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```
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## 🌏 Inference under the Interactive Simulation Mode
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To run the world model in an interactive simulation mode, follow these steps:
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- **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):
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```
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world_model_interaction_prompts/
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├── images
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│ ├── dataset1_name
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│ │ ├── 0.png # Image prompt
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│ │ └── ...
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│ └── ...
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├── transitions
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│ ├── dataset1_name
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│ │ ├── meta_data # Used for normalization
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│ │ ├── 0.h # Robot state and action data; in interaction mode,
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│ │ │ # only used to retrieve the robot state corresponding
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│ │ │ # to the image prompt
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│ │ └── ...
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│ └── ...
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├── dataset1_name.csv # File for loading image prompts, text instruction and corresponding robot states
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└── ...
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```
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- **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)
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- **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, lanuch the inference with the command:
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```
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bash scripts/run_world_model_interaction.sh
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```
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## 📝 Codebase Architecture
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Here's a high-level overview of the project's code structure and core components:
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```
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unitree-world-model/
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├── assets # Media assets such as GIFs, images, and demo videos
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├── configs # Configuration files for training and inference
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│ ├── inference
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│ └── train
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├── examples # Example inputs and prompts for running inference
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├── external # External packages
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├── prepare_data # Scripts for dataset preprocessing and format conversion
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├── scripts # Main scripts for training, evaluation, and deployment
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├── src
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│ ├──unitree_worldmodel # Core Python package for the Unitree world model
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│ │ ├── data # Dataset loading, transformations, and dataloaders
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│ │ ├── models # Model architectures and backbone definitions
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│ │ ├── modules # Custom model modules and components
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│ │ └── utils # Utility functions and common helpers
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```
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## 🙏 Acknowledgement
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Lots of code are inherieted 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).
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