Hyung-Tae Seo

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

A residual reinforcement learning DOB framework that pairs an analytical observer with an RL policy is proposed that derived and enforced a state-dependent action bound on the RL policy from an input-to-state stability (ISS) analysis such that the closed loop provably confines the tracking error to a certified envelope...