X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment
  Vision-Language-Action Model
Authors: name: Jinliang Zheng, name: Jianxiong Li, name: Zhihao Wang, name: Dongxiu Liu, name: Xirui Kang, name: Yuchun Feng, name: Yinan Zheng, name: Jiayin Zou, name: Yilun Chen, name: Jia Zeng, name: Ya-Qin Zhang, name: Jiangmiao Pang, name: Jingjing Liu, name: Tai Wang, name: Xianyuan Zhan

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/