通过频谱引导让扩散模型生成更平滑的机器人动作
Frequency-Guided Action Diffusion via Sub-Frequency Manifold Traversal
- 用子频带流形逐步引导扩散过程，抑制高频噪声
- 在5个基准的15个任务上提升动作平滑度与时间一致性
- 适合需要精细动作控制的机器人学习场景
通过行为克隆学习视觉-运动策略通常依赖人类操作者收集的示范轨迹。然而，自然的人类示范本身包含高频噪声，如间歇性抖动、停顿和动作抖动。直接模仿这些原始轨迹会导致模型继承这些次优行为。这一问题在基于扩散的策略中尤为显著，其迭代去噪步骤可能无意放大高频伪影，损害有意义的细粒度细节。为此，我们提出一种新型频域算法，实现隐式频谱调控与平滑动作生成。我们的方法——频率引导算子（FGO），通过逐步将噪声样本导向具有扩展频带的中间子频带流形，引导扩散策略的生成过程。在5个基准的15个机器人操控任务上验证，FGO在提升动作平滑性和时间一致性的同时，保留了成功执行任务所需的必要细节。
原文摘要 · Abstract (English)
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/
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