Kaiwen Hong
Papers
6
Total Citations
108
H-Index
4
About
Kaiwen Hong is an emerging robotics researcher whose work spans social robot navigation, assistive technologies, and robot learning. His most significant contributions lie in developing intelligent systems that enable robots to operate safely and effectively alongside humans in complex, real-world environments. Hong is perhaps best known for his attention-based interaction graph framework for intention-aware crowd navigation, which addresses critical limitations in reinforcement learning-based navigation by explicitly modeling diverse agent interactions and pedestrian intentions — work that has garnered 76 citations and established him as a notable voice in human-robot coexistence research. Building on this foundation, his HEIGHT framework further extends robot navigation to constrained environments with static obstacles, capturing richer spatial and temporal relationships. Beyond navigation, Hong has demonstrated breadth through DRAGON, a dialogue-based assistive robot that leverages visual-language grounding to aid persons with visual impairments — reflecting a strong commitment to socially impactful robotics. His earlier work on kinaesthetic surgical training systems and more recent research on human-agent collaborative learning for robot manipulation highlight an enduring interest in human-machine interaction and skill transfer. Across these contributions, Hong consistently bridges perception, planning, and human-centered design to push the boundaries of assistive and autonomous robotics.
Research Focus
Key Achievements
Top Papers
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