Landan Seguin
Papers
2
Total Citations
48
H-Index
2
About
Landan Seguin’s research lies at the intersection of robotic perception, manipulation, and autonomous navigation, with a focus on enabling robots to interact intelligently with unstructured environments. His most influential work, “Toward Affordance Detection and Ranking on Novel Objects for Real-World Robotic Manipulation” (2019, 32 citations), introduces a groundbreaking framework that segments and ranks affordances—action possibilities—on previously unseen objects. By leveraging region-based affordance segmentation, this work equips robots with the ability to infer manipulation strategies from visual data alone, a critical step toward truly autonomous grasping and tool use in dynamic settings. Seguin also made significant contributions to aerial robotics through “A Deep Learning Approach to Localization for Navigation on a Miniature Autonomous Blimp” (2020, 16 citations), where he developed a deep learning-based localization system for the Georgia Tech Miniature Autonomous Blimp (GT-MAB). This system enables indoor navigation without reliance on external motion capture, advancing the practicality of lightweight, safe aerial platforms. With a combined 48 citations across these key papers, Seguin’s work demonstrates clear impact in bridging perception and action for real-world robotics.
Research Focus
Key Achievements
Top Papers
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