CNN Based Reliable Classification of Household Chores Objects for Service Robotics Applications Household chores service is one of the desirable functions for a service robot. Object classification is the most important function when searching for objects. We consider that the major concern is that the robot should not misclassify the object. If the robot misclassifies household object images, it will then perform the household tasks with the wrong object. This may cause serious damages to a service robot and to the user. This concept gives us the insight that the precision of the classification must be very high by setting a confidence threshold so that it then can claim a reliable service robot. By exploring this concept, we develop a more convincing indicator, Classification Reliability, to reveal the reliability of deep learning model. Moreover, we develop a finetune rule base to continuously regenerate more proper training dataset for the CNN model to increase reliability. Experimental results demonstrate that the CNN model finetuned by our closedloop system achieves the reliability which is higher than the other similar effects such as DenseNet on the CIFAR10 dataset.

Adaptive Loss Balancing for Multitask Learning of Object Instance Recognition and 3D Pose Estimation Object instance recognition and 3D pose estimation are important elements in robot vision technology. Stateoftheart methods improve the accuracy of both instance recognition and pose estimation using multitask learning. These methods use unified balancing parameters to integrate the loss of each task, which means task difficulties are the same for all objects. However, the method we propose can adjust the balancing parameters for each object. This idea is based on the assumption that task difficulties are different for each object, since the distinctiveness of object instances and poses depends on their appearance and shape. Our method sequentially estimates task difficulties for CNN based on the amount of loss change and calculates balancing parameters for each object. Our experiments show that our method improves the accuracy of both object instance recognition and pose estimation compared with stateoftheart methods using the common LineMOD dataset.