Teleoperated Omni-directional Dual Arm Mobile Manipulation Robotic System with Shared Control for Retail Store

The swiftly expanding retail sector is increasingly adopting autonomous mobile robots empowered by artificial intelligence and machine learning algorithms to gain an edge in the competitive market. However, these autonomous robots encounter challenges in adapting to the dynamic nature of retail products, often struggling to operate autonomously in novel situations. In this study, we introduce an omni-directional dual-arm mobile robot specifically tailored for use in retail environments. Additionally, we propose a tele-operation method that enables shared control between the robot and a human operator. This approach utilizes a Virtual Reality (VR) motion capture system to capture the operator's commands, which are then transmitted to the robot located remotely in a retail setting. Furthermore, the robot is equipped with heterogeneous grippers on both manipulators, facilitating the handling of a wide range of items. We validate the efficacy of the proposed system through testing in a mockup of retail environment, demonstrating its ability to manipulate various commonly encountered retail items using both single and dual-arm coordinated manipulation techniques.

Enhancing Automated and Early Detection of Alzheimer's Disease Using Out-Of-Distribution Detection

More than 10.7% of people aged 65 and older are affected by Alzheimer's disease. Early diagnosis and treatment are crucial as most Alzheimer's patients are unaware of having it until the effects become detrimental. AI has been known to use magnetic resonance imaging (MRI) to diagnose Alzheimer's. However, models which produce low rates of false diagnoses are critical to prevent unnecessary treatments. Thus, we trained supervised Random Forest models with segmented brain volumes and Convolutional Neural Network (CNN) outputs to classify different Alzheimer's stages. We then applied out-of-distribution (OOD) detection to the CNN model, enabling it to report OOD if misclassification is likely, thereby reducing false diagnoses. With an accuracy of 98% for detection and 95% for classification, our model based on CNN results outperformed our segmented volume model, which had detection and classification accuracies of 93% and 87%, respectively. Applying OOD detection to the CNN model enabled it to flag brain tumor images as OOD with 96% accuracy and minimal overall accuracy reduction. By using OOD detection to enhance the reliability of MRI classification using CNNs, we lowered the rate of false positives and eliminated a significant disadvantage of using Machine Learning models for healthcare tasks. Source code available upon request.