Gaze-Based Teleoperation with Intent Inference Model for Robotic Manipulators

Abstract

Eye gaze-based control interfaces provide a non-invasive means of enhancing human-robot collaboration for activities of daily living and can reduce the cognitive burden on operators performing complex tasks. Eye gaze has traditionally been used for "gaze triggering," where fixating on an object activates pre-programmed robotic movements. In this work, we propose a gaze-based robotic teleoperation approach that utilizes real-time gaze data to guide the freeform movement of robotic manipulators. The proposed approach incorporates a Gaussian Mixture Regression (GMR)-based intent inference model to capture the nonlinear relationship between gaze data and the operator’s intended robotic movements. For benchmarking, we further implemented a Gaussian Hidden Markov Model (G-HMM) to provide a comparable probabilistic framework for intent inference. Experimental results demonstrate that the GMR-based approach achieves a statistically significant improvement over G-HMM in terms of control efficiency, trajectory smoothness against involuntary eye fluctuations, as well as enhancing the user’s sense of involvement and control.