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About

Prashant Doshi is a researcher whose work spans artificial intelligence, multi-robot systems, and STEM education technology, with particular depth in inverse reinforcement learning (IRL) and autonomous decision-making. His most widely cited contribution — a 2015 study on robotics in elementary education with 338 citations — demonstrates a striking interdisciplinary range, showing how robotic tools can meaningfully enhance pre-service teachers' STEM engagement and pedagogical confidence. This work was complemented by the RoboSTEM portal initiative, designed to provide open educational resources supporting robotics-integrated lesson design. On the technical frontier, Doshi has made sustained contributions to multi-robot inverse reinforcement learning, particularly tackling the challenging problem of learning robot behaviors under occlusion — where agents are only partially observable. His series of papers from 2015 through 2020 progressively refined IRL methods to handle real-world constraints like agent interactions, state transition estimation, and online learning. His SA-Net framework further advances learning from observation by enabling robust state-action recognition from streaming sensory data. Later work on decision-theoretic planning in open multiagent systems addresses dynamic, unpredictable agent environments relevant to collaborative robotics. Collectively, Doshi's research bridges foundational AI methodology with practical robotic applications and educational impact.