Joichiro Sumiyoshi
Papers
2
Total Citations
16
H-Index
2
About
Joichiro Sumiyoshi is a robotics researcher whose work focuses on advancing autonomous navigation for mobile robots, particularly through the integration of deep learning and motion planning. His primary research areas include obstacle avoidance, discrete motion control, and end-to-end learning for robotic systems. Sumiyoshi’s major contributions lie in developing neural network-based motion planners that enable robots to navigate complex environments, such as dead-end spaces, using discrete commands like straight, right, and left—mimicking human operator control. His 2022 paper on a deep recurrent neural network for obstacle avoidance in dead-end environments has garnered 9 citations, while his foundational 2020 work on an end-to-end discrete motion planner, which proposed a fully-connected deep neural network approach, has received 7 citations. These studies highlight his innovative shift from traditional continuous control to discrete motion strategies, improving robot adaptability in confined or challenging spaces. Sumiyoshi’s research is notable for its practical applications in autonomous systems, offering a bridge between human-like control logic and machine learning efficiency. His work continues to influence the field of mobile robotics, providing a robust framework for safer and more reliable navigation.
Research Focus
Key Achievements
Top Papers
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- 2