Stability-aware Residual Reinforcement Learning Framework for Robotic Manipulator Disturbance Compensation

Although conventional controllers and disturbance observers (DOBs) are the standard for precision tracking in manipulators, they suffer from parameter uncertainty, nonlinear friction, and compound disturbances. This study proposes a residual reinforcement learning DOB framework that pairs an analyti