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About

Antonio Roberto is a leading researcher in social robotics, specializing in the development of perceptive and interactive systems that enable robots to understand and engage with humans naturally. His work lies at the intersection of artificial intelligence, computer vision, and audio analysis, with a focus on creating robots that can recognize emotions, identities, and intentions in real time. His most cited paper, "A Social Robot Architecture for Personalized Real-Time Human–Robot Interaction" (2023, 24 citations), proposes a framework that integrates IoT and AI to enable realistic, context-aware conversations. Roberto has also made significant contributions to emotion recognition from facial expressions (18 citations) and multi-task deep learning for voice-based soft biometrics, including gender, age, and emotion recognition (15 citations). His innovative work on few-shot speaker re-identification and efficient transformers for on-robot natural language understanding addresses critical challenges in deploying AI on resource-constrained robotic platforms. Additionally, his development of the DegramNet architecture for learnable time–frequency audio representations and the creation of challenging voice datasets for noisy environments have advanced the robustness of robotic perception. With a growing citation record and a focus on practical, real-world applications, Roberto is shaping the future of cognitive robotics and human-robot collaboration.