Haptic Teleoperation in Extended Reality for Electric Vehicle Battery Disassembly Using Gaussian Mixture Regression

ABSTRACT We present a comprehensive teleoperation framework for electric vehicle (EV) battery cell handling, integrating haptic feedback, extended reality (XR) visualization, and task‐parameterized Gaussian mixture regression (TP‐GMR) for adaptive, real‐time trajectory generation. The system enables seamless switching between manual and autonomous operation through a variable autonomy mechanism, while constraint barrier functions (CBFs) enforce spatial safety constraints. A lightweight intent prediction module anticipates user deviation and precomputes corrective trajectories, reducing response time from 2.0 s to under 1 ms. The framework is implemented on an industrial KUKA robotic manipulator and validated in structured and real‐world EV battery disassembly scenarios. Results show that combining XR and haptic feedback reduces task completion time by up to 48% and path deviation by 32%, compared to manual teleoperation without assistance. Predictive replanning improves continuity of force feedback and reduces unnecessary user motion. The integration of XR‐based spatial computing, learning‐from‐demonstration, and real‐time control enables safe, precise, and efficient manipulation in high‐risk environments. This study demonstrates a scalable human‐in‐the‐loop solution for battery recycling and other semi‐structured tasks, where full automation is impractical. The proposed system significantly improves operator performance while maintaining safety and flexibility, marking a meaningful advancement in collaborative field robotics.