Papers
14
Total Citations
547
H-Index
8
About
Kuniaki Noda is a leading researcher in robotics and artificial intelligence, specializing in multimodal integration, deep learning, and sensory-motor coordination for humanoid robots. His pioneering work focuses on enabling robots to learn complex behaviors by fusing information from vision, touch, and sound—much like humans do. Noda’s most influential paper, “Multimodal integration learning of robot behavior using deep neural networks” (2014, 196 citations), demonstrates how deep neural networks can combine sensory inputs to improve robotic perception and action. He further advanced tactile sensing with “Tactile object recognition using deep learning and dropout” (111 citations), addressing object recognition when vision is unreliable. His earlier work on dynamic neural networks for object handling (126 citations) laid the foundation for interactive robot manipulation. Noda also contributed to emotional communication in robotics, notably with the WAMOEBA-2R project, and explored sound source separation for robot audition. His research has profound implications for creating robots that can operate autonomously in human environments, bridging the gap between perception and action through deep learning. With over 500 total citations, Noda’s work continues to inspire advances in human-robot interaction and embodied AI.
Research Focus
Key Achievements
Top Papers
- 1Multimodal integration learning of robot behavior using deep neural networks196 citations · 2014
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- 3Tactile object recognition using deep learning and dropout111 citations · 2014
- 4Associated Emotion and Its Expression in an Entertainment Robot QRIO22 citations · 2004
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- 8Sound source separation for robot audition using deep learning11 citations · 2015
- 9Intersensory Causality Modeling Using Deep Neural Networks7 citations · 2013
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