GazeGrasp: DNN-Driven Robotic Grasping with Wearable Eye-Gaze Interface
We present GazeGrasp, a gaze-based manipulation system enabling individuals with motor impairments to control collaborative robots using eye-gaze. The system employs an ESP32 CAM for eye tracking, MediaPipe for gaze detection, and YOLOv8 for object localization, integrated with a Uni-versal Robot UR10 for manipulation tasks. After user-specific calibration, the system allows intuitive object selection with a magnetic snapping effect and robot control via eye gestures. Experimental evaluation involving 13 participants demonstrated that the magnetic snapping effect significantly reduced gaze alignment time, improving task efficiency by 31%. GazeGrasp provides a robust, hands-free interface for assistive robotics, enhancing accessibility and autonomy for users.
Authors
- Muhammad Haris Khan (ORCID: https://orcid.org/0000-0002-0275-1524)
- Miguel Altamirano Cabrera (ORCID: https://orcid.org/0000-0002-5974-9257)
- Luis Moreno‐Izquierdo (ORCID: https://orcid.org/0000-0003-3260-5883)
- Dzmitry Tsetserukou (ORCID: https://orcid.org/0000-0001-8055-5345)
- Issatay Tokmurziyev
Institutions
- Skolkovo Institute of Science and Technology (RU)
Publication Details
- Published
- 2025-03-04
- DOI
- https://doi.org/10.1109/hri61500.2025.10974227
- Citations
- 6
- Primary Topic
- Gaze Tracking and Assistive Technology
- Type
- article
- Field-Weighted Citation Impact
- 11.62
Funders
- Russell Sage Foundation