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About

Krishna Kumar Narayanan is a robotics researcher whose work sits at the intersection of computer vision, autonomous navigation, and machine learning for mobile robotic systems. His research has made significant contributions to enabling robots to perceive and navigate complex environments using purely vision-based approaches, with a particular focus on omnidirectional imaging. Narayanan's most influential work, "Floor Segmentation of Omnidirectional Images for Mobile Robot Visual Navigation" (2010, 24 citations), introduced a supervised learning framework that allows robots to identify traversable floor regions and avoid obstacles in real time — a foundational challenge in autonomous navigation. This was complemented by his ensemble-based approach to floor-obstacle segmentation, which improved robustness by fusing multiple heterogeneous classifiers. Beyond perception, Narayanan has explored how robots can learn behaviors directly from human demonstrations, with contributions to robot programming by demonstration (15 citations) and situated, context-specific behavior learning. His work on acquiring behavioral dynamics from demonstrations reflects a broader commitment to making robot programming more intuitive and adaptive. With a body of work spanning visual segmentation, learning from demonstration, and scenario-aware robot behavior, Narayanan's research offers practical pathways toward more autonomous, perceptually capable mobile robots — making his publications valuable reading for students in robotics and computer vision.