Abstract
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 disturbance context, organizing the latent space by disturbance regime and enabling rapid adaptation across disturbance transitions. To guarantee stability, we 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 for arbitrary policy outputs. Experiments on a 6-DOF manipulator demonstrated consistent improvements in disturbance estimation and tracking, including a 27.8% tracking-error reduction on real hardware under zero-shot sim-to-real transfer and a 38.0% reduction under a base-vibration disturbance that was not observed during training.
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- Open access
- Green open access
Cite this article
APA 7
Kim, J., Kwon, J., Kim, H. S., Seo, T., & Seo, H. T. (2026). Stability-aware Residual Reinforcement Learning Framework for Robotic Manipulator Disturbance Compensation. https://omanscience.com/en/articles/stability-aware-residual-reinforcement-learning-framework-for-robotic-manipulator-disturbance-compensation
MLA 9
Kim, Jihong, et al. "Stability-aware Residual Reinforcement Learning Framework for Robotic Manipulator Disturbance Compensation." https://omanscience.com/en/articles/stability-aware-residual-reinforcement-learning-framework-for-robotic-manipulator-disturbance-compensation.
Chicago (author–date)
Kim, Jihong, Joonhyuk Kwon, Hwa Soo Kim, TaeWon Seo, and Hyung-Tae Seo. 2026. "Stability-aware Residual Reinforcement Learning Framework for Robotic Manipulator Disturbance Compensation." https://omanscience.com/en/articles/stability-aware-residual-reinforcement-learning-framework-for-robotic-manipulator-disturbance-compensation.
Harvard
Kim, J., Kwon, J., Kim, H. S., Seo, T. and Seo, H. T. (2026) 'Stability-aware Residual Reinforcement Learning Framework for Robotic Manipulator Disturbance Compensation', Available at: https://omanscience.com/en/articles/stability-aware-residual-reinforcement-learning-framework-for-robotic-manipulator-disturbance-compensation.
Vancouver
Kim J, Kwon J, Kim HS, Seo T, Seo HT. Stability-aware Residual Reinforcement Learning Framework for Robotic Manipulator Disturbance Compensation. https://omanscience.com/en/articles/stability-aware-residual-reinforcement-learning-framework-for-robotic-manipulator-disturbance-compensation
IEEE
J. Kim, J. Kwon, H. S. Kim, T. Seo, and H. T. Seo, "Stability-aware Residual Reinforcement Learning Framework for Robotic Manipulator Disturbance Compensation," https://omanscience.com/en/articles/stability-aware-residual-reinforcement-learning-framework-for-robotic-manipulator-disturbance-compensation.