| # 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 | |
| lerobot-train \ | |
| --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). | |