Robot Learning

Support Vector Machine

A support vector machine is a supervised learning algorithm that finds the maximum-margin hyperplane separating classes, using the kernel trick to obtain nonlinear decision boundaries in implicit high-dimensional feature spaces (Cortes and Vapnik, 1995). Before deep learning, SVMs over hand-crafted features were standard in robotics for grasp stability classification, terrain and traversability classification, and object recognition. They remain useful for small-data problems with engineered features.

Why it matters for physical AI

Small labeled datasets are common at the edges of robotics — fault detection, contact classification — where margin-based methods on good features still beat data-hungry networks.