Hanna Pasula

Papers

Total Citations

131

H-Index

About

Hanna Pasula is a researcher whose work bridges robotics, probabilistic modeling, and artificial intelligence. Her primary research areas include topological mapping for autonomous systems, relational learning, and planning under uncertainty. Pasula’s most influential contribution is the introduction of **Voronoi random fields (VRFs)**, a novel technique for extracting the topological structure of indoor environments. This work, published in 2007 and cited over 126 times, enables mobile robots to build meaningful maps by labeling places based on spatial layout—a foundational step for autonomous navigation in complex indoor settings. Beyond mapping, Pasula has explored **learning and planning with probabilistic relational rules**, applying these models to simulated worlds like blocks-world environments. Her research demonstrates a commitment to integrating statistical learning with symbolic reasoning, allowing robots to infer action dynamics and generate flexible plans. While her citation impact is led by the VRF paper, her broader work contributes to the development of intelligent systems that can understand and interact with physical spaces. For students and researchers in robotics and AI, Pasula’s work exemplifies how probabilistic graphical models can solve real-world spatial reasoning problems.