Francesco Giuliari

Italian Institute of Technology, University of Verona

Papers

Total Citations

H-Index

About

Francesco Giuliari is a researcher advancing the frontier of autonomous robotics, with a primary focus on **Active Visual Search (AVS)** in indoor environments. His work addresses the critical challenge of enabling robots to efficiently locate objects in unknown or partially known spaces through intelligent, online motion planning. Giuliari’s major contribution is the development of the **POMP** and **POMP++** frameworks, which leverage Partially Observable Monte Carlo Planning (POMCP) to compute optimal search policies. POMP, his foundational work, enables an agent to plan its next move using only its current pose and an RGB-D frame, achieving effective search in known environments. The follow-up, POMP++, extends this to unknown indoor settings, learning an online policy that adapts in real-time. Collectively, these papers have garnered over **20 citations**, demonstrating their impact on the robotics and computer vision communities. Giuliari’s work is notable for bridging the gap between theoretical planning algorithms and practical, real-world robotic search, offering a scalable solution for applications like search-and-rescue, warehouse automation, and domestic service robots. His innovative use of POMCP in AVS marks a significant step toward more autonomous and perceptive machines.