David Hogg
Papers
17
Total Citations
486
H-Index
10
About
David Hogg is a robotics and AI researcher whose work spans autonomous systems, human activity recognition, natural language grounding, and qualitative spatial reasoning. He is perhaps best known for his contributions to the STRANDS Project, a landmark initiative in long-term robot autonomy that demonstrated how service robots could operate reliably in real-world environments over extended periods — a paper that has garnered nearly 200 citations and stands as a cornerstone of the field. His early work on automated 3D interior reconstruction (1998) was ahead of its time, combining laser and video data acquisition in both push-trolley and robotic platforms. Hogg has made significant strides in enabling robots to understand human activity through unsupervised learning techniques, developing frameworks that allow mobile robots to generalise motion patterns without labeled data. He also co-created QSRlib, a widely adopted software library for extracting qualitative spatial relations from video. More recently, his research has tackled the challenge of natural language acquisition for robots — teaching systems to ground language in visual experience through grammar induction. Across his career, Hogg's research consistently bridges perception, cognition, and embodied interaction, with real-world deployment at its heart.
Research Focus
Key Achievements
Top Papers
- 1The STRANDS Project: Long-Term Autonomy in Everyday Environments196 citations · 2017
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- 4Natural Language Acquisition and Grounding for Embodied Robotic Systems39 citations · 2017
- 5Unsupervised human activity analysis for intelligent mobile robots31 citations · 2019
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- 9Unsupervised Learning of Qualitative Motion Behaviours by a Mobile Robot13 citations · 2016
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