Harsh Yadav
Papers
1
Total Citations
2
H-Index
1
About
Harsh Yadav is a robotics researcher whose work lies at the intersection of artificial intelligence and autonomous systems, with a primary focus on mapless navigation for mobile robots. His most-cited paper, "Deep Reinforcement Learning for Mapless Navigation of Autonomous Mobile Robot" (2023), addresses a critical challenge in intralogistics: enabling robots to navigate to goals without any prior environmental map. By developing a deep reinforcement learning-based controller, Yadav demonstrated how robots can learn adaptive navigation policies purely through interaction, eliminating the need for expensive pre-mapping. This contribution is particularly significant for dynamic industrial settings where environments change frequently. Though early in his career, his work has already garnered attention, with his flagship paper accumulating citations that signal growing interest in his approach. His research bridges the gap between theoretical reinforcement learning and practical robotics, offering scalable solutions for warehouse automation and autonomous material handling. As the field moves toward more flexible, self-learning systems, Yadav's contributions provide a foundation for robots that can operate intelligently in unknown spaces, marking him as an emerging voice in autonomous navigation research.
Research Focus
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Top Papers
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