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About this paper
Junlin Wang
The Frequency Guidance Operator (FGO) framework improves diffusion-based visuomotor policies by explicitly controlling spectral content during denoising, allowing for learning from noisy human demonstrations. It generates smoother, more temporally consistent robot actions, achieving higher task success rates across various manipulation benchmarks.
Key insights
- Explicitly steering the diffusion denoising process through a hierarchy of expanding sub-frequency manifolds effectively attenuates high-frequency noise while preserving critical fine-grained details.
- Learning and guiding towards specific frequency bands enables robust policy generation from imperfect expert data, addressing the challenge of overfitting to suboptimal high-frequency variations.
- Coupling the diffusion step with the allowable frequency band during training (KFC sampling) efficiently allocates model learning capacity, focusing on low-frequency structures at high noise levels and gradually incorporating high-frequency details.
Summary by alphaXiv.
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