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About

Bogdan Moldovan is a robotics and artificial intelligence researcher whose work centers on **relational affordance modeling**, robotic manipulation, and statistical relational learning. His most influential contribution, "Learning Relational Affordance Models for Robots in Multi-Object Manipulation Tasks" (2012, 108 citations), fundamentally advanced how robots perceive and act upon their environments by extending affordance theory beyond single-object interactions to complex, multi-object configurations — a critical step toward enabling robots to handle real-world manipulation scenarios. Building on this foundation, Moldovan has consistently pushed the boundaries of affordance-based robotics, exploring how relational affordances can improve occluded object search (2014, 30 citations), inform sequential manipulation planning, and scale to two-arm robotic systems. His integration of probabilistic reasoning through frameworks like ProbLog into affordance modeling represents a particularly notable bridge between symbolic AI and robotics, allowing robots to reason under uncertainty with structured, interpretable models. With over 190 cumulative citations, Moldovan's body of work has shaped how the robotics community thinks about action-centered perception and adaptive manipulation. His research is especially valuable for students exploring the intersection of cognitive robotics, probabilistic reasoning, and autonomous manipulation systems.