Open Access

Anke Fischer-Janzen

• People with severe disabilities strive for a more independence to improve their quality of life. Due to an aging society, the number of people dependent on daily care is expected to increase in the coming years. In response, research in human-robot interaction (HRI) focuses on investigating factors in user experience with robot controls and developing user-oriented robot applications to automatePeople with severe disabilities strive for a more independence to improve their quality of life. Due to an aging society, the number of people dependent on daily care is expected to increase in the coming years. In response, research in human-robot interaction (HRI) focuses on investigating factors in user experience with robot controls and developing user-oriented robot applications to automate daily tasks. Providing people with severe disabilities with robots that can perform everyday tasks would reduce the workload of caregivers and family members while increasing users' independence. This work focuses on eye-tracking-driven controls for assistive robotic arms. Using eye-tracking as an input modality offers several advantages: it is noninvasive, requires minimal equipment investment, and allows for quick calibration compared to other input modalities. These benefits facilitate the deployment of such controls. This work identified many approaches to operate a robot with gaze. These approaches differed in the way gaze was employed to control the robot and in the level of effort required to issue commands. A major challenge of eye-tracking-driven controls is achieving three-dimensional control of the robotic arm using two-dimensional eye movements. One solution is to grant the robot more autonomy and enabling it to perform tasks independently. In this case, the user only needs to initiate the desired task. Such an outcome can be achieved through object-oriented gaze or user intent recognition methods. It was found that ambiguous information can result when gaze is tracked on objects that are used in different tasks. Considering how the system is used in everyday life, it is evident that robust task selection is difficult in an unstructured environment with many available tasks. A second challenge in designing eye-tracking controls is instructing the robot to interact correctly with an object. Many approaches use 3D-gaze estimation to determine the object's location relative to the eye-tracker. This requires high accuracy and precise calibration to minimize error. This doctoral thesis investigated the user experience with eye-tracking-driven controls and feasible control strategy solutions to address the challenges that were identified. The focus rested on creating a scalable control architecture that addresses usability and robustness challenges. This approach overcomes the key limitations identified in object-oriented selection methods. The resulting framework combines gaze-based interaction with semantically structured task representations and architectural safeguards against perceptual uncertainty. The framework allows for free head movement, reducing dependency on precise head tracking and additional sensors, thereby supporting accessible robotic systems.…