Robot Learning

Backpropagation

Backpropagation is the algorithm for computing gradients of a loss function with respect to every parameter of a neural network by applying the chain rule backward through the computation graph, layer by layer. Popularized for training multilayer networks by Rumelhart, Hinton, and Williams in 1986, it is the engine of essentially all deep learning, implemented as reverse-mode automatic differentiation in frameworks like PyTorch and JAX.

Why it matters for physical AI

Every learned robot policy, from grasp detectors to vision-language-action models, is trained by backpropagation, and differentiable simulators extend the same machinery to computing gradients through physics itself.