Robot Learning

Stochastic Gradient Descent (SGD)

Stochastic gradient descent (SGD) is an optimization algorithm that updates model parameters using gradients estimated from small random mini-batches of data rather than the full dataset, making large-scale neural network training tractable. Variants adding momentum and adaptive per-parameter learning rates, most prominently Adam and AdamW, dominate practice. Virtually every learned robot policy, perception model, and world model is trained with some member of this family.

Why it matters for physical AI

Optimizer behavior shapes robot learning outcomes concretely — training stability on small demonstration datasets, fine-tuning dynamics of pretrained VLAs, and convergence of RL losses all trace back to SGD mechanics.