Papers
18
Total Citations
367
H-Index
10
About
Barry Ridge is a robotics researcher whose work spans two deeply interconnected domains: cognitive and developmental robotics, with a particular focus on affordance learning, and flexible robotic manufacturing systems. His most influential contribution is a comprehensive taxonomy of computational affordance models in robotics (2017, 81 citations), grounded in J.J. Gibson's ecological psychology, which has become an essential reference for researchers bridging perception and robot action. Complementing this theoretical foundation, Ridge has consistently developed practical systems for affordance learning, pioneering self-supervised and unsupervised approaches that allow robots to autonomously build internal representations of object interactions without labeled data — a significant step toward truly autonomous developmental systems. On the applied manufacturing side, Ridge has made substantial contributions to reconfigurable robot workcells, addressing the growing industrial need for flexible, rapidly adaptable automation. His work on smart hardware integration and software reconfiguration (2020, 71 citations) has proven particularly timely and impactful. Additional contributions include robot learning by demonstration, trajectory adaptation for assembly tasks, and simulation-aided human-robot handover prediction using deep learning. Across more than a decade of research, Ridge has consistently worked at the intersection of cognitive science, machine learning, and practical robotics engineering, making him a distinctive and versatile voice in the field.
Research Focus
Key Achievements
Top Papers
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- 5Rapid hardware and software reconfiguration in a robotic workcell26 citations · 2017
- 6A System for Learning Basic Object Affordances using a Self-Organizing Map13 citations · 2008
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- 8Self-Supervised Online Learning of Basic Object Push Affordances12 citations · 2015
- 9Unsupervised Learning of Basic Object Affordances from Object Properties12 citations · 2009
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