Papers
3
Total Citations
78
H-Index
3
About
Teham Buiyan is a leading researcher at the intersection of autonomous navigation and deep reinforcement learning (DRL), with a primary focus on bridging the gap between cutting-edge AI and practical robotics. His major contribution lies in developing frameworks that allow DRL-based obstacle avoidance to be seamlessly integrated into conventional autonomous navigation systems, addressing the critical challenge of deploying flexible, learning-based planners in real-world, highly dynamic environments. Buiyan’s work, such as the highly cited “Arena-Rosnav” (50 citations), introduces novel control switches that combine state-of-the-art planners, enabling mobile robots to adaptively choose between traditional and DRL methods for superior performance. His research has been instrumental in moving beyond overly conservative planning approaches, promising more efficient and robust navigation for logistics, delivery, and assistance robots. With over 78 combined citations on his core papers, Buiyan is recognized for his practical, deployment-oriented innovations, making him a key figure in advancing the real-world utility of deep reinforcement learning for autonomous systems.
Research Focus
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Top Papers
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