Papers
3
Total Citations
22
H-Index
3
About
Xinkai Jiang is a researcher at the intersection of robotics, human-robot interaction, and crowd-aware navigation, with a focus on making autonomous systems safer and more intuitive in dynamic public spaces. His work addresses two critical challenges: detecting abnormal pedestrian behavior to prevent group hazards, and enabling robots to learn new tasks efficiently through augmented reality interfaces. In his 2021 paper on abnormal pedestrian trajectory detection, Jiang developed methods for inspection robots to recognize anomalous movements in crowded environments like stations and hospitals, a contribution cited 11 times for its relevance to public safety. His 2024 user study on AR-based data collection interfaces for robot learning, with 8 citations, explores how virtual and augmented reality can streamline the gathering of task demonstrations, advancing the field of robot learning from human input. Earlier, in 2020, Jiang introduced a navigation probability map based on an influencer recognition model, addressing the challenge of robot path planning in highly dynamic pedestrian environments. Through these contributions, Jiang is shaping safer, more adaptable robots that can understand and respond to complex human behaviors.
Research Focus
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