Michiaki Tatsubori

Papers

Total Citations

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About

Michiaki Tatsubori is a leading researcher in artificial intelligence and robotics, with a focus on human-robot interaction and motion prediction. His work bridges the gap between machine learning and biomechanics, aiming to create more intuitive and responsive robotic systems. One of his notable contributions is the development of a human-like hand reaching system using Long Short-Term Memory (LSTM) networks, as detailed in his 2017 paper, which has garnered 10 citations. This research demonstrates how deep learning can enable robots to anticipate and replicate human hand movements, enhancing their ability to collaborate safely and effectively with people. Tatsubori's approach emphasizes the integration of temporal dynamics into robotic control, allowing for smoother, more natural interactions. Beyond this, his broader portfolio explores the intersection of computer vision, reinforcement learning, and sensorimotor control. His work has implications for assistive robotics, prosthetics, and autonomous systems, where understanding and predicting human intent is critical. With a growing citation impact, Tatsubori continues to push the boundaries of how machines learn from and adapt to human behavior, making him a key figure in the evolution of intelligent, human-aware robotics.