Perception

FPN (Feature Pyramid Network)

An FPN (Feature Pyramid Network) is a convolutional network architecture, introduced by Lin et al. (2017), that builds a multi-scale feature pyramid by combining a backbone's high-resolution shallow features with semantically strong deep features through top-down pathways and lateral connections. FPNs became a standard component of object detectors and segmentation models such as Faster R-CNN and Mask R-CNN, enabling robust recognition of objects across widely varying scales.

Why it matters for physical AI

Robots encounter task-relevant objects at wildly different apparent sizes as distance varies; multi-scale feature architectures underpin the detectors robots use for grasp target localization and scene understanding.