Diffusion Policy Visuomotor Policy Learning Via Action Diffusion — Paper Explained
The problem statement Diffusion Policy solves is Visuomotor manipulation...
Introduction We all know diffusion models like DALL-E and Stable Diffusion for their ability to generate stunning images by iteratively removing noise. But what if we applied that exact same principle to robotic control? Diffusion Policy is a groundbreaking approach to visuomotor manipulation that adapts the DDPM architecture to solve imitation learning. Instead of converting a latent vector into an image, it learns to denoise a random sequence into a highly accurate “action chunk”—a trajectory of 7-DoF end-effector poses. By conditioning this denoising process on camera observations rather than text prompts, Diffusion Policy gracefully handles the multi-modal, non-Markovian nature of...
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