Robot Learning

Graph Neural Network (GNN)

A graph neural network is a neural architecture that operates on graph-structured data by iteratively passing and aggregating messages along edges, producing node, edge, or graph-level representations that respect the graph's relational structure. Robotics applications include learned physics simulators such as DeepMind's graph network simulators, policies that generalize across robot morphologies by treating the kinematic tree as a graph, multi-robot coordination, and reasoning over object-centric scene graphs.

Why it matters for physical AI

Robots, scenes, and contact dynamics are naturally relational; graph-structured models offer a route to policies and simulators that generalize across morphologies and object counts where fixed-size networks cannot.