Durgakant Pushp

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About

Durgakant Pushp is a robotics researcher whose work spans autonomous navigation, multi-robot systems, and decision-making under real-world constraints. His primary contributions lie in bridging the gap between perception and deployment, particularly through domain adaptation for traversability prediction. His most cited work, "CALI: Coarse-to-Fine ALIgnments Based Unsupervised Domain Adaptation of Traversability Prediction for Deployable Autonomous Navigation" (2022, 7 citations), introduces a novel framework that reduces performance drops when autonomous vehicles encounter unfamiliar environments, enabling more reliable off-road navigation. Pushp also developed a UAV-miniUGV hybrid system (2022, 7 citations) that combines aerial and ground robots for hidden area exploration and manipulation—a task impossible for either platform alone. More recently, his work on "Decision-Making Among Bounded Rational Agents" (2024, 2 citations) extends game-theoretic models to account for cognitive limitations, with implications for human-robot interaction and multi-agent coordination. Though early in his career, Pushp’s focus on deployable, real-world robotics—from traversability to heterogeneous teams—demonstrates a commitment to making autonomous systems robust and practical. His work is particularly relevant for researchers in field robotics, domain adaptation, and multi-agent systems.