Namiko Saito
Papers
12
Total Citations
86
H-Index
5
About
Namiko Saito is a leading roboticist whose research focuses on enabling robots to perform complex, contact-rich manipulation tasks in unstructured, real-world environments. Her work sits at the intersection of deep learning, sensorimotor coordination, and tool use, with a particular emphasis on integrating multimodal sensory data—including vision, force, and tactile feedback—to achieve dexterous, adaptive behaviors. Saito’s major contributions include pioneering deep learning models for wiping 3D objects (18 citations), where her system learns to trace object shapes without pre-designed computational models, and for real-time liquid pouring (10 citations), where a robot estimates unknown liquid dynamics through sensorimotor coordination. She has also advanced tool-use models that enable robots to autonomously select and manipulate tools based on environmental context (12 citations), and developed structured motion generation for long-horizon tasks like tidying (7 citations). Her recent work on few-shot learning of force-based motions (5 citations) and explicit contact optimization in whole-body manipulation (4 citations) pushes the boundaries of robotic adaptability. With over 80 total citations and a growing portfolio of high-impact publications, Saito is a rising star in robotic manipulation, recognized for her innovative integration of deep learning and physical interaction.
Research Focus
Key Achievements
Top Papers
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- 3Tool-Use Model Considering Tool Selection by a Robot Using Deep Learning12 citations · 2018
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- 8Explicit Contact Optimization in Whole-Body Contact-Rich Manipulation4 citations · 2024
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