Jenish Thapa
Papers
1
Total Citations
2
H-Index
1
About
Jenish Thapa is an emerging researcher in artificial intelligence and robotics, with a focus on human-robot collaboration and reinforcement learning. His work addresses the critical challenge of enabling autonomous robots to navigate dynamic, real-world environments—such as construction sites—where they must coordinate with humans and adapt to moving obstacles. Thapa’s most cited paper, “Applying grid world based reinforcement learning to real world collaborative transport” (2024), introduces an AI-driven framework that translates grid-world learning models into practical collaborative transportation tasks. This contribution bridges the gap between simulated reinforcement learning and physical deployment, demonstrating how robots can learn to approach and transport objects alongside human partners. Though early in his career, with 2 citations on this work, Thapa’s research holds significant promise for advancing autonomous systems in complex industrial settings. His approach offers a scalable pathway for integrating AI into logistics and construction, highlighting his potential to shape future human-robot interaction technologies.
Research Focus
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Top Papers
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