Ruiqi Wang
Papers
2
Total Citations
22
H-Index
2
About
Ruiqi Wang is an emerging researcher at the intersection of human-robot interaction, autonomous navigation, and multi-agent systems. Their work focuses on developing intelligent robotic systems capable of operating safely and effectively alongside humans in complex, real-world environments. Wang's most recognized contribution, NaviSTAR, introduced a socially aware robot navigation framework that combines hybrid spatio-temporal graph transformers with preference learning to help robots interpret and respond to pedestrian expectations and social norms — a critical challenge in deploying robots in crowded public spaces. This work has garnered 18 citations, reflecting its relevance to the growing field of socially intelligent robotics. More recently, Wang has extended their research into multi-human multi-robot (MHMR) collaboration, tackling the difficult problem of adaptive task allocation under team heterogeneity and dynamic information uncertainty — an issue that existing approaches had largely left unresolved. With citations already accumulating on this newer work, Wang demonstrates a consistent trajectory toward solving foundational challenges in human-robot teaming. Their research has meaningful implications for autonomous systems deployed in healthcare, logistics, search-and-rescue, and other domains requiring seamless human-robot coordination.
Research Focus
Key Achievements
Top Papers
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- 2