Model Comparison for Fast Domain Adaptation in Table Service Scenario

In restaurants, many aspects of customer service, such as greeting customers, taking orders, and processing payments, are automated. Due to the various cuisines, required services, and different standards of each restaurant, one challenging part of making the entire automated process is inspecting and providing appropriate services at the table during a meal. In this paper, we demonstrate an approach for automatically checking and providing services at the table. We initially construct a base model to recognize common information to comprehend the context of the table, such as object category, remaining food quantity, and meal progress status. After that, we add a service recognition classifier and retrain the model using a small amount of local restaurant data. We gathered data capturing the restaurant table during the meal in order to find a suitable service recognition classifier. With different inputs, combinations, time series, and data choices, we carried out a variety of tests. Through these tests, we discovered that the model with few significant data points and trainable parameters is more crucial in the case of sparse and redundant retraining data.

Balancing Domain Gap for Object Instance Detection

Object instance detection in cluttered indoor environment is a core functionality for service robots. We can readily build a detection system by following recent successful strategy of deep convolutional neural networks, if we have a large annotated dataset. However, it is hard to prepare such a huge dataset in instance detection problem where only small number of samples are available. This is one of main impediment to deploying an object detection system. To overcome this obstacle, many approaches to generate synthetic dataset have been proposed. These approaches confront the domain gap or reality gap problem stems from discrepancy between source domain (synthetic training dataset) and target domain (real test dataset). In this paper, we propose a simple approach to generate a synthetic dataset with minimum human effort. Especially, we identify that domain gaps of foreground and background are unbalanced and propose methods to balance these gaps. In the experiment, we verify that our methods help domain gaps to balance and improve the accuracy of object instance detection in cluttered indoor environment.