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.