Shelves are common in homes, warehouses, and commercial settings due to their storage efficiency. However, this efficiency comes at the cost of reduced visibility and accessibility. When looking from a side (lateral) view of a shelf, most objects will be fully occluded, resulting in a constrained lateralaccess mechanical search problem. To address this problem, we introduce: (1) a novel bluction tool, which combines a thin pushing blade and suction cup gripper, (2) an improved LAX-RAY simulation pipeline and perception model that combines ray-casting with 2D Minkowski sums to efficiently generate target occupancy distributions, and (3) a novel SLAX-RAY search policy, which optimally reduces target object distribution support area using the bluction tool. Experimental data from 2000 simulated shelf trials and 18 trials with a physical Fetch robot equipped with the bluction tool suggest that using suction grasping actions improves the success rate over the highest performing push-only policy by 26% in simulation and 67% in physical environments.

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Background Reidentification of prior nodules for temporal comparison is an important but time-consuming step in lung cancer screening. We develop and evaluate an automated nodule detector that utilizes the axial-slice number of nodules found in radiology reports to generate high precision nodule predictions. Methods 888 CTs from Lung Nodule Analysis were used to train a 2-dimensional (2D) object detection neural network. A pipeline of 2D object detection, 3D unsupervised clustering, false positive reduction, and axial-slice numbers were used to generate nodule candidates. 47 CTs from the National Lung Cancer Screening Trial (NLST) were used for model evaluation. Results Our nodule detector achieved a precision of 0.962 at a recall of 0.573 on the NLST test set for any nodule. When adjusting for unintended nodule predictions, we achieved a precision of 0.931 at a recall 0.561, which corresponds to 0.06 false positives per CT. Error analysis revealed better detection of nodules with soft tissue attenuation compared to ground glass and undeterminable attenuation. Nodule margins, size, location, and patient demographics did not differ between correct and incorrect predictions. Conclusions Utilization of axial-slice numbers from radiology reports allowed for development of a lung nodule detector with a low false positive rate compared to prior feature-engineering and machine learning approaches. This high precision nodule detector can reduce time spent on reidentification of prior nodules during lung cancer screening and can rapidly develop new institutional datasets to explore novel applications of computer vision in lung cancer imaging.

Aiming at the inability to accurately predict the remaining useful life of rolling bearings due to the phased degradation in the bearing degradation process, this paper proposes a local-global cooperative learning strategy to solve the problem that the information cannot be fully utilized due to the characteristics of the data. And the strategy is combined with the least squares support vector machine to build a regression model, which improves the accuracy of the remaining useful life of bearings. The strategy evaluates the health state of bearing’s degradation process based on singular value decomposition and kurtosis criteria to divide each degradation stage of the bearing so that the degradation information of each stage of the bearing can be learned. Then, according to the proposed cooperative learning mechanism, the local learning of each stage of the bearing is extended to the global learning of the total degradation process of the bearing. Finally, the learning strategy is applied to the least squares support vector machine model to predict the remaining useful life of bearings better. The results on the PHM2012 dataset show that our method’s values of root mean square error and mean absolute percentage error are 35.4461 and 0.2041, respectively.