Robot Learning

MLP

An MLP (multilayer perceptron) is the elementary feedforward neural network architecture: stacked fully connected layers with nonlinear activations mapping fixed-size input vectors to outputs. Despite their simplicity, MLPs remain ubiquitous in robotics as policy and value networks for state-based reinforcement learning, as action and prediction heads atop transformer or convolutional backbones, and as the feedforward blocks inside every transformer layer.

Why it matters for physical AI

Most reinforcement-learned locomotion controllers deployed on real legged robots are small MLPs, prized for microsecond inference and predictable behavior on embedded compute where large architectures cannot run.