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

arXiv · AI, language, vision and robotics · article · Sep 18, 2026 · UTC

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 analytical observer with an RL policy. The deterministic baseline operates within a reliable region, whereas the RL policy explicitly targets the residuals that the model cannot capture. To make this compensation disturbance-aware, an estimator network aligns the observation history with a privileged distu

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