Yutaro Ishida
Papers
5
Total Citations
55
H-Index
4
About
Yutaro Ishida is a robotics researcher whose work sits at the intersection of computer vision, hardware acceleration, and domestic service robotics. His primary research areas include semi-automatic dataset generation for deep neural networks, hardware intelligent processing accelerators, and VLSI implementation of analog computing models for robotic systems. Ishida’s major contributions include developing a method for semi-automatically generating training datasets for object detection and recognition, specifically designed for domestic service robots, which reduces the manual labor typically required for such tasks. He has also pioneered the use of time-domain analog computing with transient states (TACT) to create energy-efficient hardware accelerators for intelligent processing on robots. His most cited works, including papers on dataset generation and hardware acceleration, have garnered up to 15 citations each, demonstrating steady impact in the field. Ishida is a key member of the Hibikino-Musashi@Home team, which has consistently competed in RoboCup@Home since 2010, and his work on hardware-software co-design for robot middleware packages highlights his commitment to practical, deployable solutions for domestic service robots.
Research Focus
Key Achievements
Top Papers
- 1
- 2Hibikino-Musashi@Home 2017 Team Description Paper15 citations · 2017
- 3A hardware intelligent processing accelerator for domestic service robots14 citations · 2020
- 4
- 5