Filippo Dalmonte

Papers

Total Citations

H-Index

About

Filippo Dalmonte is a researcher in computer vision and robotics, with a focus on advancing autonomous systems through data-driven methods. His work centers on enhancing robotic perception and manipulation, particularly in the domain of grasping—the ability for machines to interact with objects in unstructured environments. Dalmonte’s key contribution lies in developing techniques to improve video-based grasping classification, where he introduced a novel approach that leverages automatic annotations to generate large-scale, high-quality training data. This method reduces the reliance on costly manual labeling, enabling more efficient and scalable learning for robotic systems. His most-cited paper, "Video Grasping Classification Enhanced with Automatic Annotations" (2021), has garnered 2 citations, reflecting its foundational role in this emerging area. While early in his career, Dalmonte’s work addresses a critical bottleneck in robotics: bridging the gap between simulation and real-world performance. His research holds promise for applications in manufacturing, healthcare, and service robotics, where precise and adaptive grasping is essential. Dalmonte’s contributions underscore a commitment to making robotic systems more autonomous and capable, paving the way for smarter, more responsive machines.