Papers
13
Total Citations
336
H-Index
9
About
Haruhisa Okuda is a leading researcher in industrial robotics, specializing in bin picking, robotic assembly, and 3D vision for complex manufacturing environments. His most influential work, "Fast graspability evaluation on single depth maps for bin picking with general grippers," has garnered 151 citations and introduced a method to estimate graspability from a single depth map, enabling robots to handle randomly piled objects with universal grippers. Okuda has made significant contributions to automating the assembly of flexible cables with connectors—a notoriously difficult task—through innovative 3D sensing and force control techniques, as seen in his highly cited papers on cable handling and connector insertion. His research also addresses pose estimation in heavy clutter using multi-flash cameras (32 citations) and robust grasping strategies for parts of various shapes (38 citations). Notably, Okuda developed a multi-gripper switching strategy for bin picking diverse items and a Rao-Blackwellized particle filtering method for 6-DOF localization in robotic assembly. His work has directly advanced industrial automation, enabling robots to handle specular, flexible, and irregular objects with high precision, making him a key figure in bridging computer vision and robotic manipulation for real-world production systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Robust grasping strategy for assembling parts in various shapes38 citations · 2014
- 3Pose estimation in heavy clutter using a multi-flash camera32 citations · 2010
- 4
- 5Finding a needle in a specular haystack18 citations · 2011
- 6
- 7
- 8A robotic assembly system capable of handling flexible cables with connector11 citations · 2011
- 9
- 10Bin-picking System for General Objects8 citations · 2015