Unsupervised Task Segmentation Approach for Bimanual Surgical Tasks using Spatiotemporal and Variance Properties In surgical workflow analysis and training in robotassisted surgery, automatic task segmentation could significantly reduce the manual labeling time and enhance robot learning efficiency. This paper presents an unsupervised segmentation approach to automatically segment a given surgical task without manual intervention. A new segmentation method is presented, which relies only on bimanual kinematic trajectories without the need for prior information about the data. Specifically, surgical tasks are segmented by fusing trajectoriesxe2x80x99 spatiotemporal and variance properties. To demonstrate the effectiveness of the proposed method, detailed experiments were first conducted on our dataset. We segmented trajectories of three different surgical stitches and observed an average F 1  score of 77.9% against the ground truths. The same trajectories were then added with different levels of noises and the segmentation comparison was made with four other methods. The proposed algorithm had demonstrated its robustness against the noises. Finally, to assess its generalization ability, the method was evaluated on publicly available JIGSAWS dataset and an average F 1  score of 75.5% was achieved.

DESK: A Robotic Activity Dataset for Dexterous Surgical Skills Transfer to Medical Robots Datasets are an essential component for training effective machine learning models. In particular, surgical robotic datasets have been key to many advances in semiautonomous surgeries, skill assessment, and training. Simulated surgical environments can enhance the data collection process by making it faster, simpler and cheaper than real systems. In addition, combining data from multiple robotic domains can provide rich and diverse training data for transfer learning algorithms. In this paper, we present the DESK (DExterous Surgical SKills) dataset. It comprises a set of surgical robotic skills collected during a surgical training task using three robotic platforms: the Taurus II robot, Taurus II simulated robot, and the YuMi robot. This dataset was used to test the idea of transferring knowledge across different domains (e.g. from Taurus to YuMi robot) for a surgical gesture classification task with seven gesturessurgemes. We explored two different scenarios: 1) No transfer and 2) Domain transfer (simulated Taurus to real Taurus and YuMi robots). We conducted extensive experiments with three supervised learning models and provided baselines in each of these scenarios. Results show that using simulation data during training enhances the performance on the real robots, where limited real data is available. In particular, we obtained an accuracy of 55% on the real Taurus data using a model that is trained only on the simulator data, but that accuracy improved to 82% when the ratio of real to simulated data was increased to 0.18 in the training set.