Robot Learning

LSTM

LSTM (long short-term memory) is a recurrent neural network architecture, introduced by Hochreiter and Schmidhuber in 1997, that uses gated memory cells with input, forget, and output gates to preserve information over long sequences and mitigate vanishing gradients. Before transformers became dominant, LSTMs were the standard choice for sequence modeling, and they remain in use where compact recurrent state and strictly streaming inference are advantageous.

Why it matters for physical AI

Recurrent policies carry a fixed-size memory across timesteps, suiting partially observable control on compute-limited hardware, and they remain common in reinforcement-learned locomotion controllers that must run at kilohertz rates.