# 用于 VLA 适应的噪声空间学习

> 原文链接：https://robotics.hellobike.com/papers/2026-10-09-2610-13943
> 频道：papers | 更新日期：2026-10-09 | 标签：arxiv, cs.RO
> 核心摘要：提出 NSL 在固定预训练 VLA 下学习上下文条件噪声分布，并在 LIBERO、RoboTwin 及四项真机操作任务上验证。

# 用于 VLA 适应的噪声空间学习

::: v-pre
> 原文链接：[Noise Space Learning for VLA Adaptation](https://arxiv.org/abs/2610.13943)（arXiv 2610.13943 · 发布日期：2026-10-09）

**Noise Space Learning for VLA Adaptation**

提出 NSL 在固定预训练 VLA 下学习上下文条件噪声分布，并在 LIBERO、RoboTwin 及四项真机操作任务上验证。

## 核心要点

- 预训练 VLA 具备广泛操作先验，但面向特定任务适应时受机器人交互、计算资源和安全约束影响。
- NSL 面向基于流匹配的 VLA，通过学习上下文条件噪声分布实现适应，同时保持预训练 VLA 固定。
- 该接口可容纳不同学习算法，论文分别以 PPO、SAC 和 AWR 实例化，并给出信息论分析。
- 实验在 LIBERO、RoboTwin 和四项真机操作任务上研究 NSL 变体，并总结不同资源与部署条件下的选择建议。

**验证范围与局限**：验证范围包括 LIBERO、RoboTwin 和四项真实机器人操作任务，均处于交互、计算与执行约束下。

## 英文摘要 (Abstract)

Pretrained vision-language-action (VLA) models encode broad manipulation priors, yet adapting them to specific tasks requires balancing limited robot interaction, computational resources, and safety constraints. Noise space learning (NSL) adapts flow-matching-based VLAs by learning a context-conditioned distribution over noise while keeping the pretrained VLA fixed. Building on prior noise-steering methods, we formulate NSL as an interface for VLA adaptation that accommodates different learning algorithms and instantiate it with PPO, SAC, and AWR. Theoretically, we provide an information-theoretic analysis of how NSL enables efficient adaptation. Empirically, we study these NSL algorithms on LIBERO, RoboTwin, and four real-robot manipulation tasks under interaction, computation, and execution constraints. Across these settings, we develop a suite of effective NSL variants and derive practical recommendations for choosing among them under different resource and deployment regimes.

作者：Junjie Lu, Chuheng Zhang, Yucen Wang, Yuhua Jiang, Xinyao Qin, Min Xu, Li Zhao
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## 原始参考来源 (Sources)

- [arXiv 2610.13943：Noise Space Learning for VLA Adaptation](https://arxiv.org/abs/2610.13943)