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modify the README file for wallx (#2705)
* support wallx * fix bugs in flow * incorporate wallx model into lerobot * update the policy methods * reduce to least config and params & pass lerobot basic test * fixed dtype bugs * add wallx dependencies * update * remove flash-attn requirement && fix bug in inference and fast mode * fix bug for inference * add some small modifications * fix pre-commit errors * remove lerobot[wallx] * fix ci * fix precommit issues * fix: exclude wallx extra properly in CI workflows * fix: add uv conflicts for wallx transformers version * fix: peft test import * pre-commit * only export WallXConfig from wall_x package to avoid peft import in CI * remove torch dep * precommit * add import * update doc files * fix minor errors --------- Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: vincentchen <chenlufang@x2robot.com> Co-authored-by: Geoffrey19 <sympathischmann35@gmail.com> Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Pepijn <pepijn@huggingface.co>
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Tong Wu
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# WALL-OSS
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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.
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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).
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## Model Overview
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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.
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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:
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- **Embodied perception–enhanced multimodal pretraining**: Large-scale training on unified vision–language–action data to strengthen spatial, causal, and manipulation understanding.
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- **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.”
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- **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.
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- **Two-stage training paradigm**:
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- **Inspiration stage**: Injecting discrete action priors to strengthen spatial understanding and semantic-action alignment.
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- **Integration stage**: Using flow matching to achieve high-frequency continuous control.
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## Installation Requirements
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1. Install LeRobot by following our [Installation Guide](./installation).
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2. Install WallX dependencies by running:
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```bash
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pip install -e ".[wallx]"
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```
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## Usage
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To use WallX in LeRobot, specify the policy type as:
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```python
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policy.type=wall_x
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```
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## Training
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For training WallX, you can use the standard LeRobot training script with the appropriate configuration:
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```bash
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python src/lerobot/scripts/lerobot_train.py \
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--dataset.repo_id=your_dataset \
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--policy.type=wall_x \
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--output_dir=./outputs/wallx_training \
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--job_name=wallx_training \
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--policy.repo_id=your_repo_id \
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--policy.pretrained_name_or_path=x-square-robot/wall-oss-flow \
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--policy.prediction_mode=diffusion \
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--policy.attn_implementation=eager \
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--steps=3000 \
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--policy.device=cuda \
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--batch_size=32
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```
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### Training Arguments
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| Argument | Description |
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| ------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `--dataset.repo_id` | The Hugging Face Hub repository ID for your training dataset (e.g., `lerobot/aloha_sim_insertion_human`) |
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| `--policy.type` | Specifies using the WallX policy architecture |
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| `--output_dir` | Local directory where training checkpoints and logs will be saved |
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| `--job_name` | A name identifier for this training run (used in logging/tracking) |
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| `--policy.repo_id` | Your Hugging Face Hub repo ID where the trained model will be pushed |
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| `--policy.pretrained_path` | Path to pretrained WallX weights to initialize from (the official WALL-OSS checkpoint) |
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| `--policy.prediction_mode` | The action prediction strategy: `diffusion` or `fast` - `diffusion` uses iterative denoising for action generation, `fast` uses next token prediction instead |
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| `--policy.attn_implementation` | Attention implementation backend - `eager` uses standard PyTorch attention (alternatives include `flash_attention_2` or `sdpa`) |
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| `--steps` | Total number of training steps to run |
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| `--policy.device` | Device to train on (`cuda` for GPU, `cpu` for CPU) |
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| `--batch_size` | Number of samples per training batch |
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## License
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This model follows the **Apache 2.0 License**, consistent with the original [WallX repository](https://github.com/X-Square-Robot/wall-x).
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