5 papers
Adaptive Grasping of Moving Objects in Dense Clutter via Global-to-Local Detection and Static-to-Dynamic Planning
Hao Chen, Takuya Kiyokawa, Weiwei Wan +1
Robotic grasping is facing a variety of real-world uncertainties caused by non-static object states, unknown object properties, and cluttered object arrangements. The difficulty of…
Task-Difficulty-Aware Efficient Object Arrangement Leveraging Tossing Motions
Takuya Kiyokawa, Mahiro Muta, Weiwei Wan +1
This study explores a pick-and-toss (PT) as an alternative to pick-and-place (PP), allowing a robot to extend its range and improve task efficiency. Although PT boosts efficiency i…
Efficiently Collecting Training Dataset for 2D Object Detection by Online Visual Feedback
Takuya Kiyokawa, Naoki Shirakura, Hiroki Katayama +2
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. Al…
Active Vapor-Based Robotic Wiper
Takuya Kiyokawa, Hiroki Katayama, Jun Takamatsu +1
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 transpar…
Component Selection for Craft Assembly Tasks
Vitor Hideyo Isume, Takuya Kiyokawa, Natsuki Yamanobe +3
Inspired by traditional handmade crafts, where a person improvises assemblies based on the available objects, we formally introduce the Craft Assembly Task. It is a robotic assembl…