Papers
19
Total Citations
232
H-Index
9
About
John D. Kelleher is a distinguished researcher whose work sits at the intersection of artificial intelligence, computational linguistics, and human-robot interaction. His career has focused on enabling machines to understand and respond to natural language in physically situated environments — a challenge requiring the seamless integration of spatial reasoning, visual perception, and dialogue systems. Kelleher's most influential contributions include pioneering computational models of spatial proximity and context-dependent language interpretation, with his early 2006 work on proximity modeling and visual reference resolution earning over 35 citations each and laying important groundwork for grounded language understanding. His research into human-robot dialogue — particularly how robots interpret spoken commands and recover from perception errors — has meaningfully advanced the field of task-oriented HRI, reflected in his 2021 paper garnering 44 citations. Beyond robotics and language, Kelleher has demonstrated impressive interdisciplinary reach, extending his AI expertise into healthcare, co-authoring a 2023 scoping review on technology-enabled stroke rehabilitation that highlights rehabilitation AI's growing importance. His exploration of neural language models for spatial semantics further demonstrates his engagement with cutting-edge machine learning approaches. Across more than two decades of research, Kelleher has consistently bridged theoretical linguistics and applied robotics, producing work that remains highly relevant to researchers building intelligent, language-capable systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Proximity in context36 citations · 2006
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- 6A Context-Dependent Model of Proximity in Physically Situated Environments14 citations · 2021
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- 8Structural descriptions in human-assisted robot visual learning13 citations · 2006
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