#
WALL-OSS

WALL-OSS is an open-source foundation model for embodied intelligence, proposed by the XSquare Robot team in 2025. The LeRobot implementation is adapted from their open-source WallX 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 and code.

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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.

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.

2. Install WallX dependencies by running:

       pip install -e ".[wallx]"

##
Usage

To use WallX in LeRobot, specify the policy type as:

    policy.type=wall_x

##
Training

For training WallX, you can use the standard LeRobot training script with the appropriate configuration:

    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.

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