Gaussian Process Regression for Robotic Arm Modeling

This project explores the use of Gaussian Process Regression (GPR) to model the kinematics of a robotic arm with eight input parameters (joint angles and link lengths) and a nonlinear output (tip distance from origin). Various kernel functions are evaluated, with performance analyzed via uncertainty plots and mean squared error (MSE) metrics.