X VLA Pt EU AI Act Compliance Profile

X-VLA 0.9B (Foundation Edition)

Repository: 2toINF/X-VLA

Authors: 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)

🚀 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/

⚙️ Usage

🔹 Load the model

from transformers import AutoModel

model = AutoModel.from_pretrained(
    "2toINF/X-VLA-WidowX",
    trust_remote_code=True
)

🔹 Start FastAPI server

from transformers import AutoProcessor
processor =

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Tags

Base Model