Papers
55
Total Citations
861
H-Index
16
About
Yutaka Nakamura is a prominent robotics and artificial intelligence researcher whose work spans social robotics, reinforcement learning, swarm intelligence, and biologically inspired systems. He is perhaps best known for pioneering the application of deep reinforcement learning to human-robot interaction, most notably through his development of the Multimodal Deep Q-Network (MDQN), which enables robots to acquire human-like social skills through experience — a landmark contribution that has garnered over 111 citations. His follow-up work introducing neural attention mechanisms further advanced the field of perceivable, responsive human-robot interaction. Nakamura has also made significant contributions to biologically inspired robotics, drawing on central pattern generators for bipedal locomotion control and developing the innovative "Yuragi" framework — inspired by biological fluctuation — for adaptive mobile robot search behavior. His research on swarm robotics, particularly adaptive response threshold models for task allocation and foraging optimization, demonstrates a deep engagement with collective intelligence principles drawn from social insects. With multiple highly cited works across reinforcement learning, biomechanics, and swarm systems, Nakamura's interdisciplinary approach continues to shape the development of intelligent, adaptive robotic systems capable of operating effectively alongside humans.
Research Focus
Key Achievements
Top Papers
- 1Robot gains social intelligence through multimodal deep reinforcement learning111 citations · 2016
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- 4Reinforcement learning for a CPG-driven biped robot66 citations · 2004
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