Foundation Models

Recurrent Neural Network (RNN)

A recurrent neural network (RNN) is a neural architecture with feedback connections that maintain a hidden state across time steps, allowing it to process sequences of arbitrary length with constant memory. Gated variants such as LSTM and GRU mitigate vanishing gradients. Although transformers now dominate sequence modeling, recurrent policies remain attractive on robots for their constant per-step compute and their ability to integrate observation histories under partial observability.

Why it matters for physical AI

Memory lets a policy infer unobserved quantities, such as terrain friction or object mass, from interaction history, and recurrent state-estimation modules still feature in deployed locomotion controllers where inference budgets are tight.