Uncertainty-aware Cartesian error compensation in industrial robots via point interval prediction and residual calibration

Accurate Cartesian positioning in industrial robots remains challenging under drift, friction, and regime changes. This paper presents an uncertainty-aware, two-stage compensation pipeline for a four-degree-of-freedom (4-DOF) serial robot using only joint telemetry. Stage I combines a long short-term memory (LSTM) point predictor with an auxiliary network that learns per-axis prediction intervals (PIs) via a loss balancing prediction-interval coverage probability (PICP) and mean prediction-inter