Papers
21
Total Citations
222
H-Index
8
About
Shun Nishide is a robotics and artificial intelligence researcher whose work sits at the intersection of developmental learning, neurodynamical systems, and human-robot interaction. His research draws heavily on insights from cognitive and brain science to engineer robots capable of human-like skill acquisition, with a particular focus on imitation learning, sensorimotor integration, and embodied cognition. Among his most significant contributions is a body of work on developmental imitation learning for drawing tasks, where he modeled the five recognized stages of human drawing development within robotic systems — work that has accumulated over 50 citations across related publications. His investigations into tool-body assimilation demonstrated that robots could internalize tools as extensions of their own bodies through active sensing experience, echoing findings from neuroscience on human tool use. His recurrent neural network-based framework for predicting object dynamics from visual input (cited over 45 times across two related papers) advanced autonomous robotic manipulation strategies considerably. Nishide has also extended his expertise into socially assistive robotics, contributing to conversational AI systems designed to support children with autism spectrum disorder. Spanning over a decade of research, his cumulative work reflects a consistent commitment to bridging biological principles of learning with practical robotic implementation.
Research Focus
Key Achievements
Top Papers
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- 2Tool–Body Assimilation of Humanoid Robot Using a Neurodynamical System32 citations · 2011
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- 4Inter-modality mapping in robot with recurrent neural network29 citations · 2010
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- 9Epsilon-greedy babbling5 citations · 2017
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