X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment
  Vision-Language-Action Model
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,
diverse robotic data sources, we propose a novel Soft Prompt approach with
minimally added parameters, by infusing prompt learning concepts into
cross-embodiment robot learning and introducing separate sets of learnable
embeddings for each distinct data source. These embeddings serve as
embodiment-specific prompts, which in unity empower VLA models with effective
exploitation of varying cross-embodiment features. Our new X-VLA, a neat
flow-matching-based VLA architecture, relies exclusively on soft-prompted
standard Transformer encoders, enjoying both scalability and simplicity.
Evaluated across 6 simulations as well as 3 real-world robots, our 0.9B
instantiation-X-VLA-0.9B simultaneously achieves SOTA performance over a sweep
of benchmarks, demonstrating superior results on a wide axes of capabilities,
from flexible dexterity to quick adaptation across embodiments, environments,
and tasks. Website: https://thu-air-dream.github.io/X-VLA/
68f0669336f8b025381e19cf
Jinliang Zheng
claimed_verified
2025-10-16T06:35:11.003Z
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/avatars/70f9a87d123ba65a5f931db028bb095b.svg
Jinliang Zheng
user
2toINF
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Jianxiong Li
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Zhihao Wang
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Dongxiu Liu
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Xirui Kang
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Yuchun Feng
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Yinan Zheng
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Jiayin Zou
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Yilun Chen
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Jia Zeng
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Ya-Qin Zhang
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Jiangmiao Pang
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Jingjing Liu
68f0669336f8b025381e19dc
Tai Wang
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Xianyuan Zhan
X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment
  Vision-Language-Action Model
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,
diverse robotic data sources, we propose a novel Soft Prompt approach with
minimally added parameters, by infusing prompt learning concepts into
cross-embodiment robot learning and introducing separate sets of learnable
embeddings for each distinct data source. These embeddings serve as
embodiment-specific prompts, which in unity empower VLA models with effective
exploitation of varying cross-embodiment features. Our new X-VLA, a neat
flow-matching-based VLA architecture, relies exclusively on soft-prompted
standard Transformer encoders, enjoying both scalability and simplicity.
Evaluated across 6 simulations as well as 3 real-world robots, our 0.9B
instantiation-X-VLA-0.9B simultaneously achieves SOTA performance over a sweep
of benchmarks, demonstrating superior results on a wide axes of capabilities,
from flexible dexterity to quick adaptation across embodiments, environments,
and tasks. Website: https://thu-air-dream.github.io/X-VLA/
A novel Soft Prompt approach enhances Vision-Language-Action models by using learnable embeddings for diverse robotic data, enabling superior performance across simulations and real-world robots.
Soft Prompt prompt learning cross-embodiment learnable embeddings embodiment-specific prompts flow-matching-based Transformer encoders X-VLA VLA architecture flexible dexterity quick adaptation