Papers
42
Total Citations
380
H-Index
11
About
Yu Du is a robotics researcher whose work spans several interconnected domains, including robotic perception and grasping, biologically inspired cognitive systems, mobile robot navigation, and dexterous robotic manipulation. His most-cited contribution, a 2021 paper on cascaded deep convolutional neural networks for object detection and grasping in cluttered environments (45 citations), addresses one of robotics' fundamental challenges: enabling robots to reliably grasp unknown, irregularly shaped objects in unstructured settings. Complementing this, his work on tendon-driven robotic hands and stable grasp planning reflects a sustained interest in dexterous manipulation hardware and theory. A distinctive thread running through Du's research is his application of neuroscientific principles to robotic cognition. Drawing inspiration from hippocampal spatial cells, episodic memory, and grid-place cell mechanisms, he has developed frameworks for robot navigation, cognitive mapping, and behavior planning under uncertainty — contributions that have collectively attracted dozens of citations across multiple papers. His more recent work extends into collaborative SLAM for heterogeneous UAV/UGV systems and improved path planning algorithms, demonstrating his evolution toward large-scale, multi-agent robotics. With over 200 cumulative citations, Du's research represents a thoughtful bridge between biological cognition and practical robotic autonomy.
Research Focus
Key Achievements
Top Papers
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- 4Episodic Memory-Based Robotic Planning Under Uncertainty24 citations · 2016
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- 7Design and optimization of a tendon-driven robotic hand15 citations · 2017
- 8Stable grasp planning based on minimum force for dexterous hands15 citations · 2020
- 9Robotic Episodic Cognitive Learning Inspired by Hippocampal Spatial Cells13 citations · 2020
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