Model overview
**Paper:** *Zheng et al., 2025, “X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model”* (arXiv:2510.10274) 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.
Key features
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Limitations & considerations
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Public benchmarks & run configuration
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Sources & traceability
许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。