Tommaso Apicella

University of Genoa, Queen Mary University of London

Papers

Total Citations

H-Index

About

Tommaso Apicella is a researcher advancing the intersection of computer vision, robotics, and human-robot interaction. His work focuses on enabling machines to perceive and interact with their environment more intelligently, particularly in semi-autonomous scenarios where humans remain in the loop. Apicella’s key contributions include developing a novel pipeline for affordance detection on resource-constrained devices—a critical step for deploying intelligent perception on embedded systems and mobile robots. This work, cited 5 times, addresses the challenge of predicting possible actions on objects in real-time, even with limited computational power. In another highly cited study (6 citations), he tackled the difficult problem of container localisation and mass estimation using only RGB-D cameras, overcoming challenges like occlusions and varying lighting conditions. This capability is essential for robots to safely and effectively assist humans in manipulation tasks. Apicella’s research is notable for its practical focus on bringing advanced perception to real-world, resource-limited platforms, bridging the gap between theoretical computer vision and deployable robotic systems. His work is particularly relevant for students and researchers interested in embodied AI, affordance learning, and human-aware robotics.