Gaze-Driven Assistive Robotic Manipulation with Intent Inference: From Motion to Grasp and Placement

We propose an integrated gaze-driven framework for assistive robotic manipulation that infers user intent to support fluid human-robot collaboration in semi-structured manipulation environments. The system consists of three functional components: (I) a Gaze-based Teleoperation method using Gaussian Mixture Regression (GMR) to generate smooth, intent-aligned manipulator motion; (II) a Gaze-Driven Grasp Pose Detection (GD-GPD) algorithm that fuses geometric grasp quality with gaze-derived attention using an arbitration factor ($\alpha$) to select intent-aligned grasp poses; and (III) a Gaze Guidance Pose Optimization strategy that refines placement position and release orientation to improve placement stability. In a user study with ten participants, the proposed Intent-Inference Control (IIC) mode was compared with both a Gaze-Triggered Control (GTC) mode and a Traditional Pipeline Control (TPC) mode. The results showed that IIC achieved higher target grasp accuracy and shorter completion time than GTC, demonstrating the benefit of gaze-weighted grasp candidate ranking over fixation-based target selection. Compared with TPC, IIC also improved task success rate, completion time, target grasp accuracy, and placement stability, confirming the advantage of integrating gaze-based motion guidance, intent-aware grasp selection, and placement optimization. Overall, the proposed framework provides a practical step toward more natural and responsive assistive robotic manipulation across the motion-grasp-placement sequence.