# 基于子频率流形遍历的频率引导动作扩散

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-27 08:00
- AIHOT 分数：56
- AIHOT 链接：https://aihot.virxact.com/items/cmpupuvkx007wsl3twuld7c4c
- 原文链接：https://arxiv.org/abs/2605.27919

## AI 摘要

机器人行为克隆中，人类演示数据固有的高频噪声（如抖动和停顿）会被基于扩散模型的策略继承并放大。为此，研究提出了频率引导算子（FGO）。该算子在扩散策略的生成过程中，引导噪声样本依次通过频带逐步扩展的中间子频率流形，从而实现频域上的隐式操控与平滑动作生成。在5个基准的15项机器人操作任务上验证，该方法显著提升了动作平滑度和时间一致性。

## 正文

Learning visuomotor policies via behavior cloning typically involves mimicking expert demonstrations collected by human operators. However, natural human demonstrations inherently contain high-frequency noise, such as intermittent jerks, pauses, and action jitter. Training policies to directly imitate these raw trajectories inevitably causes the model to inherit these suboptimal behaviors. This pathology is particularly pronounced in diffusion-based policies, where iterative denoising steps can inadvertently amplify high-frequency artifacts at the expense of meaningful fine-grained details. To address these limitations, we present a novel frequency-based algorithm that enables implicit spectral maneuvering and smooth action generation. Our method, Frequency Guidance Operator (FGO), steers the generation process of diffusion polices by progressively driving the noisy samples through intermediate sub-frequency manifolds with expanding spectral bands. Validated on 15 robotic manipulation tasks from 5 benchmarks, FGO achieves superior performance in enhancing action smoothness and temporal consistency while preserving the details necessary for successful task execution. Project website: https://henrywjl.github.io/frequency-guidance-operator/