Task segmentation based on transition state clustering for surgical robot assistance
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913391275147264 |
|---|---|
| author | Yamada, Yutaro Colan, Jacinto Davila, Ana Hasegawa, Yasuhisa |
| author_facet | Yamada, Yutaro Colan, Jacinto Davila, Ana Hasegawa, Yasuhisa |
| contents | Understanding surgical tasks represents an important challenge for autonomy in surgical robotic systems. To achieve this, we propose an online task segmentation framework that uses hierarchical transition state clustering to activate predefined robot assistance. Our approach involves performing a first clustering on visual features and a subsequent clustering on robot kinematic features for each visual cluster. This enables to capture relevant task transition information on each modality independently. The approach is implemented for a pick-and-place task commonly found in surgical training. The validation of the transition segmentation showed high accuracy and fast computation time. We have integrated the transition recognition module with predefined robot-assisted tool positioning. The complete framework has shown benefits in reducing task completion time and cognitive workload. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09990 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Task segmentation based on transition state clustering for surgical robot assistance Yamada, Yutaro Colan, Jacinto Davila, Ana Hasegawa, Yasuhisa Robotics Understanding surgical tasks represents an important challenge for autonomy in surgical robotic systems. To achieve this, we propose an online task segmentation framework that uses hierarchical transition state clustering to activate predefined robot assistance. Our approach involves performing a first clustering on visual features and a subsequent clustering on robot kinematic features for each visual cluster. This enables to capture relevant task transition information on each modality independently. The approach is implemented for a pick-and-place task commonly found in surgical training. The validation of the transition segmentation showed high accuracy and fast computation time. We have integrated the transition recognition module with predefined robot-assisted tool positioning. The complete framework has shown benefits in reducing task completion time and cognitive workload. |
| title | Task segmentation based on transition state clustering for surgical robot assistance |
| topic | Robotics |
| url | https://arxiv.org/abs/2406.09990 |