Optimizing Robot Programming: Mixed Reality Gripper Control
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916662638280704 |
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| author | Rettinger, Maximilian Hacker, Leander Wolters, Philipp Rigoll, Gerhard |
| author_facet | Rettinger, Maximilian Hacker, Leander Wolters, Philipp Rigoll, Gerhard |
| contents | Conventional robot programming methods are complex and time-consuming for users. In recent years, alternative approaches such as mixed reality have been explored to address these challenges and optimize robot programming. While the findings of the mixed reality robot programming methods are convincing, most existing methods rely on gesture interaction for robot programming. Since controller-based interactions have proven to be more reliable, this paper examines three controller-based programming methods within a mixed reality scenario: 1) Classical Jogging, where the user positions the robot's end effector using the controller's thumbsticks, 2) Direct Control, where the controller's position and orientation directly corresponds to the end effector's, and 3) Gripper Control, where the controller is enhanced with a 3D-printed gripper attachment to grasp and release objects. A within-subjects study (n = 30) was conducted to compare these methods. The findings indicate that the Gripper Control condition outperforms the others in terms of task completion time, user experience, mental demand, and task performance, while also being the preferred method. Therefore, it demonstrates promising potential as an effective and efficient approach for future robot programming. Video available at https://youtu.be/83kWr8zUFIQ. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02042 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimizing Robot Programming: Mixed Reality Gripper Control Rettinger, Maximilian Hacker, Leander Wolters, Philipp Rigoll, Gerhard Human-Computer Interaction Robotics Conventional robot programming methods are complex and time-consuming for users. In recent years, alternative approaches such as mixed reality have been explored to address these challenges and optimize robot programming. While the findings of the mixed reality robot programming methods are convincing, most existing methods rely on gesture interaction for robot programming. Since controller-based interactions have proven to be more reliable, this paper examines three controller-based programming methods within a mixed reality scenario: 1) Classical Jogging, where the user positions the robot's end effector using the controller's thumbsticks, 2) Direct Control, where the controller's position and orientation directly corresponds to the end effector's, and 3) Gripper Control, where the controller is enhanced with a 3D-printed gripper attachment to grasp and release objects. A within-subjects study (n = 30) was conducted to compare these methods. The findings indicate that the Gripper Control condition outperforms the others in terms of task completion time, user experience, mental demand, and task performance, while also being the preferred method. Therefore, it demonstrates promising potential as an effective and efficient approach for future robot programming. Video available at https://youtu.be/83kWr8zUFIQ. |
| title | Optimizing Robot Programming: Mixed Reality Gripper Control |
| topic | Human-Computer Interaction Robotics |
| url | https://arxiv.org/abs/2503.02042 |