| | --- |
| | license: apache-2.0 |
| | base_model: |
| | - microsoft/Florence-2-large |
| | tags: |
| | - robotics |
| | - vla |
| | pipeline_tag: robotics |
| | datasets: |
| | - Facebear/XVLA-Soft-Fold |
| | --- |
| | |
| | # X-VLA 0.9B (Soft Fold Edition) |
| |
|
| |
|
| | **Repository:** [2toINF/X-VLA](https://github.com/2toinf/X-VLA) |
| |
|
| | **Authors:** [2toINF](https://github.com/2toINF) | **License:** Apache 2.0 |
| |
|
| | **Paper:** *Zheng et al., 2025, “X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model”* ([arXiv:2510.10274](https://arxiv.org/pdf/2510.10274)) |
| |
|
| |
|
| | ## 🚀 Overview |
| |
|
| | Successful generalist **Vision-Language-Action (VLA)** models rely on effective training across diverse robotic platforms with large-scale, cross-embodiment, heterogeneous datasets. |
| | To facilitate and leverage the heterogeneity in rich robotic data sources, **X-VLA** introduces a **Soft Prompt approach** with minimally added parameters: we infuse prompt-learning concepts into cross-embodiment robot learning, introducing **separate sets of learnable embeddings** for each distinct embodiment. |
| |
|
| | These embodiment-specific prompts empower VLA models to exploit cross-embodiment features effectively. |
| | Our architecture—**a clean, flow-matching-based VLA design relying exclusively on soft-prompted standard Transformers**—achieves superior scalability and simplicity. |
| |
|
| | Trained on **Bridge Data** and evaluated across **six simulations** and **three real-world robots**, the 0.9B-parameter X-VLA simultaneously achieves **state-of-the-art performance** across diverse benchmarks, demonstrating flexible dexterity and fast adaptation across embodiments, environments, and tasks. |
| |
|
| | 🌐 **Project Website:** [https://thu-air-dream.github.io/X-VLA/](https://thu-air-dream.github.io/X-VLA/) |
| |
|
| |
|
| | <video controls autoplay loop muted playsinline width="720"> |
| | <source src="https://huggingface.co/2toINF/X-VLA-0.9B-WidowX/resolve/main/demo.mp4" type="video/mp4"> |
| | </video> |
| |
|
| | ## ⚙️ Usage |
| | ### 🔹 Load the model |
| |
|
| | ```python |
| | from transformers import AutoModel |
| | |
| | model = AutoModel.from_pretrained( |
| | "2toINF/X-VLA-WidowX", |
| | trust_remote_code=True |
| | ) |
| | ``` |
| | ### 🔹 Start FastAPI server |
| |
|
| | ```python |
| | from transformers import AutoProcessor |
| | processor = AutoProcessor.from_pretrained("2toINF/X-VLA-WidowX", trust_remote_code=True) |
| | model.run(processor, host="0.0.0.0", port=8000) |
| | ``` |
| | ### 🔹 Client-server evaluation |
| |
|
| | You can run the provided evaluation client from our GitHub: |
| | 👉 [2toINF/X-VLA – Client & Server Code](https://github.com/2toINF/X-VLA) |
| |
|
| |
|
| | ## 🧩 Architecture |
| |
|
| | | Component | Role | |
| | | :-------------------------------- | :------------------------------------------------------------------------- | |
| | | **Florence 2 Encoder** | Vision-Language representation backbone (encoder-only). | |
| | | **SoftPromptedTransformer** | Flow-matching action denoiser using learnable soft prompts per embodiment. | |
| | | **Action Hub** | Defines action spaces, masking rules, pre/post-processing, and losses. | |
| |
|
| | ## 🧠 Training Summary |
| |
|
| | | Setting | Value | |
| | | :---------------- | :---------------------------------------------- | |
| | | Training Data | SoftFold | |
| | | Parameters | ≈ 0.9 B | |
| | | Action Mode | `ee6d` | |
| | | Precision | BP16 | |
| | | Framework | PyTorch + Transformers | |
| |
|
| | --- |
| | ## 🪪 License |
| | ``` |
| | Copyright 2025 2toINF (https://github.com/2toINF) |
| | Licensed under the Apache License, Version 2.0 (the "License"); |
| | you may not use this file except in compliance with the License. |
| | http://www.apache.org/licenses/LICENSE-2.0 |
| | ``` |
| | --- |
| | ## 📚 Citation |
| | ```bibtex |
| | @article{zheng2025x, |
| | title = {X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model}, |
| | author = {Zheng, Jinliang and Li, Jianxiong and Wang, Zhihao and Liu, Dongxiu and Kang, Xirui |
| | and Feng, Yuchun and Zheng, Yinan and Zou, Jiayin and Chen, Yilun and Zeng, Jia and others}, |
| | journal = {arXiv preprint arXiv:2510.10274}, |
| | year = {2025} |
| | } |
| | ``` |
| | --- |
| | ## 🌐 Links |
| |
|
| | - 📄 **Paper:** [arXiv 2510.10274](https://arxiv.org/abs/2510.10274) |
| | - 💻 **Code & Client/Server:** [GitHub – 2toINF/X-VLA](https://github.com/2toINF/X-VLA) |
| | - 🤖 **Model Hub:** [Hugging Face – 2toINF/X-VLA-0.9B-WidowX](https://huggingface.co/2toINF/X-VLA-0.9B-WidowX) |
