具身开发者社区

外观

论文arXiv 2610.0775622 天前

StairVLA：面向 Vision-Language-Action 模型的阶段感知分层动作生成 ​

StairVLA针对VLA动作头均匀去噪的问题，以阶段感知分层生成在仿真基准和真机任务上验证降本保性能。

AI 整理
  • 本体 SDK 与控制
  • 仿真与训练
  • 具身模型

原文链接：StairVLA: Stage-Aware Hierarchical Action Generation for Vision-Language-Action Models（arXiv 2610.07756 · 发布日期：2026-10-06）

StairVLA: Stage-Aware Hierarchical Action Generation for Vision-Language-Action Models

StairVLA针对VLA动作头均匀去噪的问题，以阶段感知分层生成在仿真基准和真机任务上验证降本保性能。

核心要点 ​

  • 动机是扩散或流匹配动作头常均匀处理去噪轨迹，但不同阶段的条件关注会自然变化。
  • 方法用部分去噪动作连接长时程粗生成与局部细化，高层VLA早期去噪，轻量细化器高频修正。
  • 在LIBERO上，GR00T风格实例平均成功率由96.5%升至97.8%，每动作块延迟由115.0 ms降至44.2 ms。
  • 跨两个VLA骨干、仿真基准和真机任务，StairVLA持续降低推理成本并保持较强任务表现。

验证范围与局限：验证范围包括LIBERO、两个VLA骨干、仿真基准和真机任务。

英文摘要 (Abstract) ​

Vision-language-action (VLA) models increasingly rely on diffusion- or flow-matching-based action heads to generate continuous robot actions. These action heads typically process the denoising trajectory in a largely uniform manner. However, we observe that the conditioning focus naturally shifts across denoising stages: early stages combine language instructions and visual observations to establish a coarse action trajectory, whereas later stages place greater emphasis on current visual observations for action alignment. Based on this insight, we introduce StairVLA, a stage-aware hierarchical action generation framework that uses partially denoised actions as a natural interface between coarse long-horizon action generation and local refinement. A high-level VLA performs early denoising to produce a reusable long-horizon partially denoised action trajectory, while a lightweight refiner operates at a higher frequency to refine local action chunks using the latest observations. This design amortizes expensive high-level VLA computation while preserving frequent closed-loop correction. On LIBERO, our GR00T-style instantiation improves average success from 96.5% to 97.8% while reducing amortized inference latency from 115.0 ms to 44.2 ms per action chunk. More broadly, across two VLA backbones, simulation benchmarks, and real-robot tasks, StairVLA consistently reduces inference cost while maintaining strong task performance.

作者：Shangyuan Yuan, Xinda Qi, Yujiang Pu, Wenliang Guo, Xiaobo Tan

来源

本页事实对应的公开页面，以原文为准。

  1. arXiv 2610.07756：StairVLA: Stage-Aware Hierarchical Action Generation for Vision-Language-Action Models访问 2026-10-21