Article

Diffusion Policy: Learning Robot Manipulation Policies via Action Diffusion

Using the Push-T tabletop pushing task as a vehicle, this piece fully reconstructs the mathematical formulation of action diffusion policies, covering cosine-schedule noise addition, the noise-prediction training objective, FiLM conditioning injection, and sliding-window execution. The author also uses public pretrained weights to complete a real inference evaluation across ten seeds.

Part of the event: Tutorial on Learning Diffusion Policy Action Diffusion Strategies Released

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