# XL-VLA框架提出跨手型统一潜空间实现灵巧操作跨具身迁移

> 原文链接：https://robotics.hellobike.com/papers/2026-03-10-2603-10158-b61d1a98
> 频道：papers | 更新日期：2026-03-10 | 标签：auto, arxiv, cs.RO
> 核心摘要：针对多形态灵巧手数据孤岛难题，XL-VLA构建跨具身统一潜在动作空间，支持标准VLA架构即插即用。实验证实该框架在灵巧操作任务中性能持续优于原始关节空间基线模型，显著降低新本体数据采集成本。

# XL-VLA框架提出跨手型统一潜空间实现灵巧操作跨具身迁移

::: v-pre
> 本文由 AI 根据公开内容整理，可能有误，请以原文为准：[Cross-Hand Latent Representation for Vision-Language-Action Models](https://arxiv.org/abs/2603.10158)（arXiv 2603.10158）

## 核心内容

针对机器人灵巧操作中多形态本体数据收集成本高、难以复用的痛点，研究团队提出XL-VLA视觉-语言-动作框架。该方法核心在于构建了一个跨越不同灵巧手的统一潜在动作空间（Unified Latent Action Space），旨在解决异构具身智能体间的技能迁移难题，使现有及新采集的数据能够在同一表征下高效复用，为规模化跨具身学习提供了可扩展路径。

技术上，XL-VLA将具身无关的潜在空间设计为即插即用模块，可直接集成至标准VLA架构中，无需针对每种新手型重新训练底层策略。相比直接在原始关节空间建模的基线模型，XL-VLA通过抽象化动作表征消除了硬件差异带来的分布偏移。实验结果表明，该框架在多种灵巧操作任务上均取得更优性能，验证了统一潜空间在提升跨本体泛化能力与数据利用效率方面的有效性。

## 关键看点

- **统一潜空间**：构建跨灵巧手共享的具身无关潜在动作表征，消除异构硬件差异，支持多本体数据联合训练与高效复用
- **架构兼容性**：采用即插即用设计无缝集成至标准VLA模型，无需修改主干网络即可实现跨具身迁移，降低算法适配门槛
- **性能验证**：实验显示XL-VLA在灵巧操作任务中持续优于原始关节空间基线，证实抽象动作表征对提升泛化能力的有效性

## 工程与产业观察

该工作为具身智能“一脑多体”提供了关键算法范式。通过解耦动作语义与物理关节，XL-VLA有效缓解了新型灵巧手冷启动数据匮乏问题。对于算法工程师而言，这种统一潜空间思路可大幅降低多机型适配成本；对产业界则意味着积累的遥操作数据资产有望在不同本体间流转，加速灵巧操作技能的规模化沉淀。

## 英文摘要 (Abstract)

Dexterous manipulation is essential for real-world robot autonomy, mirroring the central role of human hand coordination in daily activity. Humans rely on rich multimodal perception--vision, sound, and language-guided intent--to perform dexterous actions, motivating vision-based, language-conditioned manipulation systems for robots. However, training reliable vision-language-action (VLA) models for dexterous manipulation requires large-scale demonstrations across many robotic hands. In addition, as new dexterous embodiments appear rapidly, collecting data for each becomes costly and impractical, creating a need for scalable cross-embodiment learning. We introduce XL-VLA, a vision-language-action framework integrated with a unified latent action space shared across diverse dexterous hands. This embodiment-invariant latent space is directly pluggable into standard VLA architectures, enabling seamless cross-embodiment training and efficient reuse of both existing and newly collected data. Experimental results demonstrate that XL-VLA consistently outperforms baseline VLA models operating in raw joint spaces, establishing it as an effective solution for scalable cross-embodiment dexterous manipulation.

作者：Guangqi Jiang, Yutong Liang, Jianglong Ye, Jia-Yang Huang, Changwei Jing, Rocky Duan, Pieter Abbeel, Xiaolong Wang, Xueyan Zou
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## 原始参考来源 (Sources)

- [arXiv 2603.10158：Cross-Hand Latent Representation for Vision-Language-Action Models](https://arxiv.org/abs/2603.10158)