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The paper introduces Immiscible Diffusion Policy, a label‑free training add‑on that prevents diffusion policies from collapsing to a single action modality. By assigning action‑noise pairs, it preserves distinct noise‑to‑action routes without altering the policy architecture or inference. Experiments on five simulated and two real‑world humanoid manipulation tasks show significant improvements in maintaining multimodal action distributions, increasing non‑dominant modality proportions by up to 14.6× and recovering missing modalities in four‑modality tasks.
arXiv:2512. 07212v3 Announce Type: replace Abstract: Imitation learning with diffusion models has advanced robotic control by capturing the multi-modal action distributions.
arXiv:2607. 17257v1 Announce Type: cross Abstract: Diffusion policies have shown strong potential for robotic imitation learning, and recent extensions incorporate additional modalities to improve manipulation performance.
We propose Ambient Diffusion Policy, a simple and principled method for imitation learning from suboptimal data in robotics. High-quality, task-specific robot data is expensive and time-consuming to collect, while suboptimal datasets with lower-quality or out-of-distribution demonstrations are abundant.
arXiv:2606. 12365v1 Announce Type: cross Abstract: We propose Ambient Diffusion Policy, a simple and principled method for imitation learning from suboptimal data in robotics.
arXiv:2606. 00336v1 Announce Type: new Abstract: We propose Parameterized Diffusion Policy (PDP), a framework for learning diffusion policies conditioned on low-dimensional, continuous parameters embedded in a learned behavior manifold.