Foundation Models

ResNet

ResNet is a convolutional neural network architecture introduced by He et al. in 2015 whose residual skip connections let gradients flow through very deep networks, enabling models of 50 to 152 layers and winning the 2015 ImageNet challenge. In robotics, compact variants such as ResNet-18 and ResNet-50 remain standard visual encoders for policies, including R3M's backbone and the image encoders of many diffusion-policy implementations.

Why it matters for physical AI

Convolutional backbones still offer strong accuracy per FLOP for onboard inference, and much of the pretrained-representation ecosystem robot learning depends on was built on ResNet features.