Robot Learning

GRU (Gated Recurrent Unit)

A GRU is a recurrent neural network cell, introduced by Cho et al. (2014), that maintains a hidden state updated through learned update and reset gates, capturing temporal dependencies with fewer parameters than the LSTM's separate cell state and three gates. In robotics, GRUs provide compact memory for partially observable control, appearing widely in recurrent reinforcement learning policies for locomotion, where they help infer unobserved quantities like terrain friction or payload from interaction history.

Why it matters for physical AI

Real robots never observe full state, and lightweight recurrent memory remains a practical choice for onboard policies that must run at high control rates, even as transformers dominate larger offline-trained models.