Perception

U-Net

U-Net is an encoder-decoder convolutional network architecture introduced by Ronneberger et al. in 2015 for biomedical image segmentation, distinguished by skip connections that pass high-resolution encoder features directly to corresponding decoder stages. The design produces dense per-pixel outputs from limited training data and has become a general-purpose backbone for segmentation, depth estimation, and grasp-affordance prediction, as well as the standard denoising network in diffusion models.

Why it matters for physical AI

Dense pixel-wise prediction underlies robot perception tasks from graspability maps to traversability estimation, and U-Net-style denoisers power the diffusion policies now prominent in manipulation learning.