mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-11 14:49:43 +00:00
Jade Choghari
473f1bd0e0
* add assets * add libero results pifast: * update * update * update size * update naems: : * update training tokenizer
81 lines
6.1 KiB
Plaintext
81 lines
6.1 KiB
Plaintext
# WALL-OSS
|
||
|
||
WALL-OSS is an open-source foundation model for embodied intelligence, proposed by the [XSquare Robot](https://x2robot.com/en/research/68bc2cde8497d7f238dde690) team in 2025. The LeRobot implementation is adapted from their open-source [WallX](https://github.com/X-Square-Robot/wall-x) repository.
|
||
|
||
X Square Robot’s WALL-OSS is now integrated into Hugging Face’s LeRobot ecosystem. This is an exciting collaborative project between the LeRobot and X Square Robot teams. You can now post-train, evaluate, and deploy WALL-OSS directly through LeRobot. With this, we’re aiming to make it easier for the open-source robotics community to customize and deploy WALL-OSS foundation models. Read and explore WALL-OSS [paper](https://arxiv.org/pdf/2509.11766) and [code](https://github.com/X-Square-Robot/wall-x).
|
||
|
||
## Model Overview
|
||
|
||
The WALL-OSS team is building the embodied foundation model to capture and compress the world's most valuable data: the continuous, high-fidelity stream of physical interaction. By creating a direct feedback loop between the model's decisions and the body's lived experience, the emergence of a truly generalizable intelligence is enabled—one that understands not just how the world works, but how to act effectively within it.
|
||
|
||
<img
|
||
src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/walloss-lerobot-paper.png"
|
||
alt="An overview of WALL-OSS"
|
||
width="85%"
|
||
/>
|
||
|
||
Technically, WALL-OSS introduces a tightly coupled multimodal architecture (tightly-coupled MoE structure) that integrates both discrete and continuous action modeling strategies. Through a two-stage training pipeline (Inspiration → Integration), the model gradually unifies semantic reasoning and high-frequency action generation. Its core innovations include:
|
||
|
||
- **Embodied perception–enhanced multimodal pretraining**: Large-scale training on unified vision–language–action data to strengthen spatial, causal, and manipulation understanding.
|
||
- **Unified Cross-Level Chain-of-Thought (Uni-CoT)**: A single differentiable framework that unifies high-level instruction reasoning, sub-task decomposition, and fine-grained action synthesis, forming a continuous chain from “understanding” to “execution.”
|
||
- **Mixture-of-Experts (MoE) action heads**: Dynamically activating experts depending on the task phase and modeling actions in discrete or continuous space to maintain stable VLM priors.
|
||
- **Two-stage training paradigm**:
|
||
- **Inspiration stage**: Injecting discrete action priors to strengthen spatial understanding and semantic-action alignment.
|
||
- **Integration stage**: Using flow matching to achieve high-frequency continuous control.
|
||
|
||
## Installation Requirements
|
||
|
||
1. Install LeRobot by following our [Installation Guide](./installation).
|
||
2. Install WallX dependencies by running:
|
||
|
||
```bash
|
||
pip install -e ".[wallx]"
|
||
```
|
||
|
||
## Usage
|
||
|
||
To use WallX in LeRobot, specify the policy type as:
|
||
|
||
```python
|
||
policy.type=wall_x
|
||
```
|
||
|
||
## Training
|
||
|
||
For training WallX, you can use the standard LeRobot training script with the appropriate configuration:
|
||
|
||
```bash
|
||
python src/lerobot/scripts/lerobot_train.py \
|
||
--dataset.repo_id=your_dataset \
|
||
--policy.type=wall_x \
|
||
--output_dir=./outputs/wallx_training \
|
||
--job_name=wallx_training \
|
||
--policy.repo_id=your_repo_id \
|
||
--policy.pretrained_name_or_path=x-square-robot/wall-oss-flow \
|
||
--policy.prediction_mode=diffusion \
|
||
--policy.attn_implementation=eager \
|
||
--steps=3000 \
|
||
--policy.device=cuda \
|
||
--batch_size=32
|
||
```
|
||
|
||
### Training Arguments
|
||
|
||
| Argument | Description |
|
||
| ------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||
| `--dataset.repo_id` | The Hugging Face Hub repository ID for your training dataset (e.g., `lerobot/aloha_sim_insertion_human`) |
|
||
| `--policy.type` | Specifies using the WallX policy architecture |
|
||
| `--output_dir` | Local directory where training checkpoints and logs will be saved |
|
||
| `--job_name` | A name identifier for this training run (used in logging/tracking) |
|
||
| `--policy.repo_id` | Your Hugging Face Hub repo ID where the trained model will be pushed |
|
||
| `--policy.pretrained_path` | Path to pretrained WallX weights to initialize from (the official WALL-OSS checkpoint) |
|
||
| `--policy.prediction_mode` | The action prediction strategy: `diffusion` or `fast` - `diffusion` uses iterative denoising for action generation, `fast` uses next token prediction instead |
|
||
| `--policy.attn_implementation` | Attention implementation backend - `eager` uses standard PyTorch attention (alternatives include `flash_attention_2` or `sdpa`) |
|
||
| `--steps` | Total number of training steps to run |
|
||
| `--policy.device` | Device to train on (`cuda` for GPU, `cpu` for CPU) |
|
||
| `--batch_size` | Number of samples per training batch |
|
||
|
||
## License
|
||
|
||
This model follows the **Apache 2.0 License**, consistent with the original [WallX repository](https://github.com/X-Square-Robot/wall-x).
|