Daisuke Miki
Papers
2
Total Citations
7
H-Index
2
About
Dr. Daisuke Miki is a pioneering researcher at the intersection of bio-inspired robotics and neuromorphic computing. His work focuses on developing energy-efficient control systems for soft robots by leveraging Spiking Neural Networks (SNNs)—a brain-inspired computational model that processes information through discrete spikes, offering dramatic power savings over traditional artificial neural networks. In his most-cited work (2024, 5 citations), Dr. Miki demonstrated how SNNs can generate lifelike crawling motions in caterpillar-like soft robots, enabling flexible, organic movements that rigid robots cannot achieve. He further advanced the field by introducing a novel "burn-in strategy" for deep reinforcement learning with SNNs (2024, 2 citations), addressing the challenge of training spiking networks for complex robotic control tasks while maintaining low power consumption. His contributions are particularly significant as they bridge the gap between theoretical neuromorphic computing and practical soft robotics applications. By showing that SNNs can effectively control deformable robots, Dr. Miki’s work opens new pathways for creating autonomous, energy-efficient soft robots for search-and-rescue, medical devices, and environmental monitoring—where flexibility and low power are critical.
Research Focus
Key Achievements
Top Papers
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