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Shingo Kitagawa

6 accepted papers

2023

Semantic Scene Difference Detection in Daily Life Patroling by Mobile Robots Using Pre-Trained Large-Scale Vision-Language Model

IROS 2023poster

It is important for daily life support robots to detect changes in their environment and perform tasks. In the field of anomaly detection in computer vision, probabilistic and deep learning methods have been used to calculate the image distance. These methods calculate distances by focusing on image…

Cited by 8SourceScholar
2022

Grasp Pose Selection Under Region Constraints for Dirty Dish Grasps Based on Inference of Grasp Success Probability through Self-Supervised Learning

ICRA 2022poster

In the literature on object grasping, the robot often determines the grasp point and posture from visual information. They predict the grasping point uniquely from the object's shape characteristics. However, as a practical matter, there are cases where there are constraints on grasp point due to th…

Cited by 2SourceScholar
2021

Miniature Tangible Cube: Concept and Design of Target-Object-Oriented User Interface for Dual-Arm Telemanipulation

RA-L 2021

In recent years, there has been a great deal of research on teleoperation of robots, and many end-effector-oriented control systems have been proposed, but these systems have difficulties in performing manipulation tasks with physical contacts between the target object, the robot, and the environmen

Cited by 5SourceScholar
2019

GraspFusion: Realizing Complex Motion by Learning and Fusing Grasp Modalities with Instance Segmentation

ICRA 2019poster

Recent progress of deep learning improved the capability of a robot to find a proper grasp of a novel object for different grasp modalities (e.g., pinch and suction). While these previous studies consider multiple modalities separately, several studies develop multi-modal grippers that can achieve s…

Cited by 22SourceScholar
2018

Instance Segmentation of Visible and Occluded Regions for Finding and Picking Target from a Pile of Objects

IROS 2018poster

We present a robotic system for picking a target from a pile of objects that is capable of finding and grasping the target object by removing obstacles in the appropriate order. The fundamental idea is to segment instances with both visible and occluded masks, which we call `instance occlusion segme…

Cited by 33SourceScholar
2018

Multi-Stage Learning of Selective Dual-Arm Grasping Based on Obtaining and Pruning Grasping Points Through the Robot Experience in the Real World

IROS 2018poster

Recently, self-supervised approach is common for robot grasping. Although this approach improves success rate, it requires a long time to execute a number of grasp trials, and single-arm grasping is only considered. However, robots can grasp more various objects with two arms, and dual-arm robots su…

Cited by 12SourceScholar