Aolei Yang
Papers
6
Total Citations
96
H-Index
5
About
Aolei Yang is a leading researcher in robotic perception, manipulation, and autonomous navigation, with a focus on bridging the gap between human-like intelligence and machine efficiency. Her most influential work, "GraspCNN" (2019, 32 citations), revolutionizes robotic grasping by introducing an oriented diameter circle representation, enabling real-time, single-shot grasp detection from RGB images—a breakthrough that simplifies complex grasp planning for industrial and service robots. Yang also pioneers human-robot collaboration, as seen in her 2021 study on humanoid motion planning using reinforcement learning (29 citations), which models robotic arm movements after human action features to enhance dexterity and adaptability. Her earlier contributions include a highly efficient grid-based path planning algorithm (2010, 14 citations) that optimizes navigation through priority-sorted direction vectors, and a comprehensive survey on 3D mapping for SLAM (2017, 9 citations). Yang’s work on multi-robot formation maneuvering (2016, 7 citations) and gaze-interactive grasping (2021, 5 citations) further demonstrates her commitment to safe, intuitive, and collaborative robotic systems. With over 96 total citations across her key publications, Yang’s research continues to shape the future of autonomous robotics, making her a vital figure in the field.
Research Focus
Key Achievements
Top Papers
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- 4Survey of 3D Map in SLAM: Localization and Navigation9 citations · 2017
- 5Multiple robots formation manoeuvring and collision avoidance strategy7 citations · 2016
- 6Collaborative Robot Grasping System Based on Gaze Interaction5 citations · 2021