Hiroshi Okumura
Papers
7
Total Citations
30
H-Index
3
About
Hiroshi Okumura is a robotics researcher whose work spans assistive technologies, computer vision, and intelligent systems. His research focuses on developing autonomous robots and AI-driven solutions to enhance accessibility and human-robot interaction. Okumura’s most cited work, “An Improvement on QR Code Limit Angle Detection using Convolution Neural Network” (2019, 12 citations), advances QR code detection for logistics and manufacturing applications. He has made significant contributions to assistive robotics, including a wearable dummy robot for physical therapy training (2013) and a smart navigation robot for the visually impaired (2025). His development of an edge-AI-based mobile robot capable of voice and object recognition (2021, 7 citations) demonstrates practical applications of AI in autonomous systems. Okumura has also explored model-based reinforcement learning with missing data (2020) and compliant-control systems for person-following robotic walkers (2023). His work on target-following robots for navigation assistance (2024) continues to push boundaries in accessible robotics. With a portfolio addressing real-world challenges in healthcare, mobility, and industrial automation, Okumura’s research exemplifies how robotics and AI can create meaningful impact for diverse user populations.
Research Focus
Key Achievements
Top Papers
- 1
- 2Object Search Using Edge-AI Based Mobile Robot7 citations · 2021
- 3
- 4Development of smart navigation robot for the visually impaired2 citations · 2025
- 5
- 6Model-based reinforcement learning with missing data2 citations · 2020
- 7