Immiscible Diffusion Policy: Preserving Multimodal Robot Actions through Label-Free Noise Assignment
When diffusion policies were first introduced, they were expected to recover multi-modal action distributions. However, we find this expectation does not always hold, as diffusion policies often collapse to a single modality even when we guarantee the balance of dataset modalities and exact within-batch symmetry. Our analysis indicates that independent action-noise pairing contributes to this failure by increasing mixing and crossing among diffusion paths, which can produce averaged denoising responses and suppress modality-specific behavior. This issue is especially severe in robot planning, where action spaces are dense and low-dimensional, significantly increasing such mixing and cross...
| Authors | Xiao Zhang, Yuxin Chen, Zhixuan Liang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 7 October 2026 |
| Last updated at source | 14 October 2026 |
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