Eye-Tracking-Driven Control in Daily Task Assistance for Assistive Robotic Arms
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911396390764544 |
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| author | Fischer-Janzen, Anke Wendt, Thomas M. Van Laerhoven, Kristof |
| author_facet | Fischer-Janzen, Anke Wendt, Thomas M. Van Laerhoven, Kristof |
| contents | Shared control improves Human-Robot Interaction by reducing the user's workload and increasing the robot's autonomy. It allows robots to perform tasks under the user's supervision. Current eye-tracking-driven approaches face several challenges. These include accuracy issues in 3D gaze estimation and difficulty interpreting gaze when differentiating between multiple tasks. We present an eye-tracking-driven control framework, aimed at enabling individuals with severe physical disabilities to perform daily tasks independently. Our system uses task pictograms as fiducial markers combined with a feature matching approach that transmits data of the selected object to accomplish necessary task related measurements with an eye-in-hand configuration. This eye-tracking control does not require knowledge of the user's position in relation to the object. The framework correctly interpreted object and task selection in up to 97.9% of measurements. Issues were found in the evaluation, that were improved and shared as lessons learned. The open-source framework can be adapted to new tasks and objects due to the integration of state-of-the-art object detection models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17404 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Eye-Tracking-Driven Control in Daily Task Assistance for Assistive Robotic Arms Fischer-Janzen, Anke Wendt, Thomas M. Van Laerhoven, Kristof Robotics 93C85 H.5.2 Shared control improves Human-Robot Interaction by reducing the user's workload and increasing the robot's autonomy. It allows robots to perform tasks under the user's supervision. Current eye-tracking-driven approaches face several challenges. These include accuracy issues in 3D gaze estimation and difficulty interpreting gaze when differentiating between multiple tasks. We present an eye-tracking-driven control framework, aimed at enabling individuals with severe physical disabilities to perform daily tasks independently. Our system uses task pictograms as fiducial markers combined with a feature matching approach that transmits data of the selected object to accomplish necessary task related measurements with an eye-in-hand configuration. This eye-tracking control does not require knowledge of the user's position in relation to the object. The framework correctly interpreted object and task selection in up to 97.9% of measurements. Issues were found in the evaluation, that were improved and shared as lessons learned. The open-source framework can be adapted to new tasks and objects due to the integration of state-of-the-art object detection models. |
| title | Eye-Tracking-Driven Control in Daily Task Assistance for Assistive Robotic Arms |
| topic | Robotics 93C85 H.5.2 |
| url | https://arxiv.org/abs/2601.17404 |