Prithwish Dan

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About

Prithwish Dan is a rising researcher at the intersection of robotics, human-robot interaction, and machine learning, whose work focuses on enabling robots to understand and adapt to human behavior in real time. His key research areas include intent prediction, joint forecasting and planning, and one-shot imitation learning. Dan’s major contributions address the fundamental challenge of bidirectional human-robot coordination. In his highly cited work “InteRACT” (2024, 6 citations), he introduced transformer models that predict human intents conditioned on robot actions, solving the chicken-or-egg problem of interdependent decision-making. His game-theoretic framework for joint forecasting and planning (2023, 4 citations) advanced safe robot motion planning by modeling the long tail of rare human motions often missed by traditional predictors. Most recently, his 2025 paper on one-shot imitation under mismatched execution tackles the practical challenge of translating human demonstrations into robot actions despite differences in movement styles and physical capabilities. Dan’s work is notable for its theoretical rigor and practical relevance, earning recognition for pushing the boundaries of autonomous systems that can collaborate seamlessly with humans.