Yutaka Katsuyama
Papers
2
Total Citations
9
H-Index
2
About
Yutaka Katsuyama is a researcher focused on overcoming the fundamental challenge of communication latency in remote robotics and monitoring systems. His primary research areas include teleoperation efficiency, real-time video prediction, and the application of deep learning to compensate for network delays in robotic control. Katsuyama's major contribution lies in developing predictive frameworks that can effectively neutralize transmission latency, bringing it to near-zero levels for practical applications. His most cited work, "A Predictive Approach for Compensating Transmission Latency in Remote Robot Control for Improving Teleoperation Efficiency" (2023, 6 citations), demonstrates the use of Long Short-Term Memory (LSTM) networks to anticipate and compensate for delays in robotic arm operations. Building on this foundation, his 2024 paper on video prediction for remote monitoring systems (3 citations) addresses the inherent impossibility of achieving true zero-latency in video streaming by predicting future frames. This work is particularly significant for industries relying on remote equipment operation, such as manufacturing, healthcare, and hazardous environment exploration. Katsuyama's research represents a practical, data-driven approach to solving one of the most persistent obstacles in teleoperation and remote monitoring.
Research Focus
Key Achievements
Top Papers
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- 2