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 semistructured 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 gazederived attention using an arbitration factor (∝) 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 gazeweighted 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.