Hiroki Katayama

Takuya Kiyokawa, Naoki Shirakura, Hiroki Katayama et al.

Training deep-learning-based vision systems require the manual annotation of a significant number of images. Such manual annotation is highly time-consuming and labor-intensive. Although previous studies have attempted to eliminate the effort required for annotation, the effort required for image collection was retained. To address this, we propose a human-in-the-loop dataset collection method that uses a web application. To counterbalance the workload and performance by encouraging the collection of multi-view object image datasets in an enjoyable manner, thereby amplifying motivation, we propose three types of online visual feedback features to track the progress of the collection status. Our experiments thoroughly investigated the impact of each feature on collection performance and quality of operation. The results suggested the feasibility of annotation and object detection.

Takuya Kiyokawa, Hiroki Katayama, Jun Takamatsu et al.

This paper presents a method for estimating normals of mirrors and transparent objects challenging for cameras to recognize. We propose spraying water vapor onto mirror or transparent surfaces to create a diffuse reflective surface. Using an ultrasonic humidifier on a robotic arm, we apply water vapor to the target object's surface, forming a cross-shaped misted area. This creates partially diffuse reflective surfaces, enabling the camera to detect the target object's surface. Adjusting the gripper-mounted camera viewpoint maximizes the extracted misted area's appearance in the image, allowing normal estimation of the target surface. Experiments show the method's effectiveness, with RMSEs of azimuth estimation for mirrors and transparent glass at approximately 4.2 and 5.8 degrees, respectively. Our robot experiments demonstrated that our robotic wiper can perform contact-force-regulated wiping motions to clean a transparent window, akin to human performance.

Takuya Kiyokawa, Hiroki Katayama, Yuya Tatsuta et al.

Owing to human labor shortages, the automation of labor-intensive manual waste-sorting is needed. The goal of automating waste-sorting is to replace the human role of robust detection and agile manipulation of waste items with robots. To achieve this, we propose three methods. First, we provide a combined manipulation method using graspless push-and-drop and pick-and-release manipulation. Second, we provide a robotic system that can automatically collect object images to quickly train a deep neural-network model. Third, we provide a method to mitigate the differences in the appearance of target objects from two scenes: one for dataset collection and the other for waste sorting in a recycling factory. If differences exist, the performance of a trained waste detector may decrease. We address differences in illumination and background by applying object scaling, histogram matching with histogram equalization, and background synthesis to the source target-object images. Via experiments in an indoor experimental workplace for waste-sorting, we confirm that the proposed methods enable quick collection of the training image sets for three classes of waste items (i.e., aluminum can, glass bottle, and plastic bottle) and detection with higher performance than the methods that do not consider the differences. We also confirm that the proposed method enables the robot quickly manipulate the objects.