Papers
15
Total Citations
154
H-Index
8
About
Qiang Zou is a robotics researcher whose work spans autonomous navigation, robot learning, and human-robot interaction, with a particular focus on bridging biological cognition and machine intelligence. His most influential contribution lies in developing biologically inspired frameworks for mobile robot navigation, drawing on hippocampal spatial mechanisms — including place cells and grid cells — to enable robots to build episodic cognitive maps and navigate under uncertainty. These neurobiologically grounded approaches, reflected in multiple highly cited works from 2017 through 2022, collectively demonstrate a sustained research thread that has garnered over 60 citations across related publications. Zou has also made significant contributions to robot manipulation, proposing reinforcement learning frameworks that improve policy acquisition efficiency and leveraging demonstration-based learning to handle environmental variability. His 2025 work on DOG-SLAM, already accumulating 20 citations, addresses dynamic environment challenges in visual SLAM through probabilistic object removal techniques. Additional contributions to omnidirectional robot odometry calibration and human-robot handover comfort modeling underscore the breadth of his expertise. Zou's research is particularly valuable for students pursuing autonomous robotics, offering rigorous methods that connect neuroscience-inspired theory with practical robotic systems.
Research Focus
Key Achievements
Top Papers
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- 6Robotic Episodic Cognitive Learning Inspired by Hippocampal Spatial Cells13 citations · 2020
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- 8Robotic Manipulation Skill Acquisition Via Demonstration Policy Learning8 citations · 2021
- 9Robotic Path Planning Based on Episodic-cognitive Map7 citations · 2019
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