Junji Oaki
Papers
29
Total Citations
275
H-Index
9
About
Junji Oaki is a robotics researcher whose work spans robot perception, manipulation, teleoperation, and autonomous control systems. He is perhaps best known for his pioneering contributions to vision-guided robotic bin-picking, with his 2020 paper on depth image–based deep learning for grasping textureless planar-faced objects garnering 45 citations — reflecting the growing industrial demand for intelligent warehouse automation. His 2022 follow-up on learning suction graspability (22 citations) and his 2024 work on multiple-suction-cup grasping further cement his reputation as a leading voice in data-driven robotic manipulation. Beyond manipulation, Oaki has made notable contributions to telerobotics, developing multi-site teleoperation systems over ISDN networks and multi-robot collaboration frameworks that address real-world challenges like communication delay. His 2002 volleyball-playing robot demonstrated an early commitment to human-robot interaction, blending dynamic tracking with socially engaging autonomous behavior. Additional contributions include teaching-less robot systems for flexible manufacturing, elastic joint identification for serial robot arms, and control strategies for in-orbit satellite assembly. Collectively, his body of work reflects a career dedicated to bridging theoretical robotics with practical, real-world deployment across industrial, space, and collaborative settings.
Research Focus
Key Achievements
Top Papers
- 1
- 2A collaborative multi-site teleoperation over an ISDN25 citations · 2003
- 3A volleyball playing robot24 citations · 2002
- 4
- 5Development of a multi-telerobot system for remote collaboration17 citations · 2002
- 6
- 7
- 8Open Robot Controller Architecture (ORCA)12 citations · 2003
- 9Decoupling Identification for Serial Two-link Robot Arm with Elastic Joints10 citations · 2009
- 10Robot control strategy for in-orbit assembly of a micro satellite8 citations · 2004