  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.
