Papers
35
Total Citations
260
H-Index
8
About
Hakaru Tamukoh is a researcher whose work bridges neuromorphic computing, hardware intelligence, and autonomous robotics, with particular expertise in brain-inspired systems and physical computing architectures. His most influential contribution explores in-materio reservoir computing using single-walled carbon nanotube–porphyrin polyoxometalate random networks, demonstrating how emergent machine intelligence can arise from unconventional physical substrates — a paper that has garnered 57 citations since 2022. Tamukoh has also made significant strides in biologically motivated computing, developing amygdala-inspired classical conditioning models and hippocampus-prefrontal cortex neural architectures implemented on FPGAs, enabling robots to acquire environment-specific knowledge from limited data. His work in hardware acceleration — including binarized convolutional neural networks and VLSI time-domain analog computing — addresses the critical challenge of deploying deep learning efficiently on resource-constrained robotic platforms. Complementing this theoretical work, Tamukoh has led the Hibikino-Musashi@Home team in competitive domestic service robotics, including a award-winning performance at the World Robot Challenge 2020. His research uniquely unifies neuroscience-inspired algorithms, custom silicon design, and real-world robotic implementation, making him a distinctive voice at the intersection of hardware intelligence and embodied AI.
Research Focus
Key Achievements
Top Papers
- 1
- 2Solution of World Robot Challenge 2020 Partner Robot Challenge (Real Space)23 citations · 2022
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- 5Hibikino-Musashi@Home 2017 Team Description Paper15 citations · 2017
- 6A hardware intelligent processing accelerator for domestic service robots14 citations · 2020
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- 8Hardware Implementation of Brain-Inspired Amygdala Model9 citations · 2019
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