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About

Pradip Pramanick is a researcher specializing in human-robot interaction, natural language understanding, and embodied AI, with a focus on making robots more intuitive and socially aware collaborators in everyday environments. His work addresses one of the central challenges in robotics: enabling machines to understand and act upon natural human communication with all its ambiguity and nuance. Pramanick's most influential contributions include his development of DoRO, a framework for disambiguating referred objects for embodied agents (19 citations), and the complementary Talk-to-Resolve system (16 citations), which combines scene understanding with spatial dialogue to resolve task ambiguities in real time. His earlier work on human-like task identification from natural conversation (15 citations) laid important groundwork for robot comprehension of informal speech. He has also advanced automatic speech recognition tailored for robotic contexts, leveraging visual and prior knowledge to improve accuracy in interactive scenarios. Beyond language understanding, Pramanick has explored social robotics, including F-formation-based group perception (7 citations) and non-intrusive fatigue detection for robot co-workers (9 citations). His Teledrive telepresence system demonstrates a practical application of embodied AI for remote caregiving. Collectively, his research shapes a vision of robots that are not merely task executors but genuinely responsive, context-aware companions.