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About

V. D. Sharma is an emerging robotics and artificial intelligence researcher whose work sits at the intersection of multi-robot systems, decentralized planning, and assistive robotics. Sharma's most significant contributions center on harnessing Graph Neural Networks (GNNs) to solve scalability challenges in decentralized multi-robot coordination — a notoriously difficult problem when robots must act collaboratively using only local communication. Their 2022 paper on GNNs for decentralized multi-robot target tracking has garnered 27 citations, establishing Sharma as a meaningful voice in this specialized domain, while earlier foundational work on submodular action selection (2021) laid important groundwork for these advances. Sharma's 2023 contribution, D2CoPlan, extends this thread into multi-robot coverage planning through differentiable decentralized learning, accumulating 12 citations and demonstrating a consistent focus on scalable, data-driven solutions. Beyond coordination, Sharma has made strides in assistive robotics with LAVA, a long-horizon visual action framework for robotic-assisted feeding that addresses the often-overlooked challenge of semi-solid food acquisition. Rounding out their portfolio is applied work in IoT-enabled robotics for hazardous environment surveillance, reflecting a researcher whose ambitions span both theoretical rigor and real-world humanitarian impact.