Peter Veto

Papers

Total Citations

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About

Peter Veto’s research lies at the dynamic intersection of human motion analysis, social robotics, and machine learning, with a focus on how non-verbal cues—especially gestures and grasping—can bridge the gap between humans and machines. His most-cited work, “Kinematic-Based Classification of Social Gestures and Grasping by Humans and Machine Learning Techniques” (2021), introduces a novel framework for classifying affective human movement into meaningful social gestures, a critical step toward enabling robots to perceive and respond to human intent without relying on language. By leveraging kinematic data and machine learning, Veto’s approach enhances human-robot interaction, making it more intuitive and socially aware. Though his citation count is still growing, this paper has already garnered 7 citations, signaling its foundational role in a nascent field. Veto’s contributions are particularly notable for their practical implications in assistive robotics and human-robot collaboration, where understanding subtle gestures can transform user experience. As a researcher, he is shaping how machines learn to “read” human motion, paving the way for more empathetic and responsive robotic systems.