Depth Image-Based Deep Learning of Grasp Planning for Textureless Planar-Faced Objects in Vision-Guided Robotic Bin-Picking. - Ping Jiang 006; Yoshiyuki Ishihara; Nobukatsu Sugiyama; Junji Oaki; Seiji Tokura; Atsushi Sugahara; Akihito Ogawa (Sensors, 2020)

Learning Suction Graspability Considering Grasp Quality and Robot Reachability for Bin-Picking. - Ping Jiang 006; Junji Oaki; Yoshiyuki Ishihara; Junichiro Ooga; Haifeng Han; Atsushi Sugahara; Seiji Tokura; Haruna Eto; Kazuma Komoda; Akihito Ogawa (Frontiers Neurorobotics, 2022)

Robotic grasp detection toward unknown objects using 3D edge detection and Gaussian mixture model for clustering candidates. - Haruki Shimotori; Ping Jiang 006; Junichiro Ooga; Atsushi Sugahara; Shun Ito; Atsuya Koike; Ryuichi Ueda (ROBIO, 2023)

Hybrid-AI Grasp Planning System that Integrates Rule-Based and DNN-Based Methods for Throughput Improvement of Picking Robots. - Kazuma Komoda; Ping Jiang 006; Haifeng Han; Junichiro Ooga; Haruna Eto; Seiji Tokura; H. Chatani; K. Sawa; Y. Oka; K. Konda; Akihito Ogawa (AIM, 2024)

MultipleCupSuctionNet: Deep Neural Network for Detecting Grasp Pose of a Vacuum Gripper with Multiple Suction Cups based on YOLO Feature Map Affine Transformation. - Ping Jiang 006; Kazuma Komoda; Haifeng Han; Ooga Junichiro (IROS, 2024)

Multiple-Object Grasping Using a Multiple-Suction-Cup Vacuum Gripper in Cluttered Scenes. - Ping Jiang 006; Junji Oaki; Yoshiyuki Ishihara; Junichiro Ooga (Robotics, 2024)