Papers

13

Total Citations

510

H-Index

10

About

Stephen Jay Gould is a prominent researcher whose work sits at the intersection of robotics, computer vision, and artificial intelligence, with particular focus on semantic understanding, visual perception, and human-robot interaction. His most influential contribution, a comprehensive survey on semantics for robotic mapping, perception, and interaction (2020), has accumulated over 140 citations across multiple venues, establishing him as a key voice in helping robots develop richer, more meaningful models of their environments. His earlier work on integrating visual and range data for object detection (2008, 88 citations) and peripheral-foveal vision for real-time object recognition (2007, 82 citations) laid important groundwork for multimodal robotic perception. Gould has also made notable advances in vision-and-language navigation (2018, 62 citations), human pose forecasting using deep Markov models, and generative modeling of 3D point clouds. More recently, his contributions to navigational visual representations and human-robot collaborative assembly systems reflect a sustained commitment to bridging perception and action in real-world robotic settings. With hundreds of citations spanning over fifteen years, Gould's research continues to shape how intelligent systems see, reason about, and interact with the physical world.

Research Focus

Key Achievements

10
H-Index
13
Papers
510
Total Citations
39
Avg Citations/Paper
🏆 Most Cited Paper
Semantics for Robotic Mapping, Perception and Interaction: A Survey
100 citations · 2020
📈 Most Prolific Year: 2017 (4 Papers)
🤝 Key Collaborators: 48
🏛 Institutions: Australian National University, Stanford Medicine, Stanford University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago