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About

Vignesh Prasad is a robotics researcher whose work sits at the intersection of human-robot interaction, machine learning, and robot motion planning. His research primarily focuses on enabling robots to interact naturally and adaptively with humans, spanning physical interactions such as handshaking and object handovers, as well as broader collaborative tasks requiring nuanced coordination. Prasad's most influential contribution is his comprehensive review of human-robot handshaking (2021, 39 citations), which has become a key reference for researchers studying physical human-robot interaction. Building on this foundation, he has developed sophisticated learning frameworks for modeling interaction dynamics, including MILD (2022, 14 citations), which leverages multimodal latent representations to help robots anticipate and react to human intentions in real time. His more recent MoVEInt framework (2024) advances this further by employing mixture models to capture the variability and complexity inherent in shared human-robot dynamics. Beyond interaction modeling, Prasad has contributed to robot navigation through reinforcement learning-based approaches to preventing monocular SLAM failure (2018). His work on bimanual handovers and few-shot action segmentation further demonstrates the breadth of his research, bridging perception, motion generation, and human-centered design to make collaborative robots more capable and intuitive partners.