Papers
1
Total Citations
2
H-Index
1
About
Subhangi is an emerging researcher in robotics and autonomous navigation, with a primary focus on reinforcement learning and visual path-following systems. Her most cited work, "Generalized Visual Path Following on Jetbot Using Normalization with Reinforcement Learning" (2024), addresses a critical challenge in robotics: enabling adaptive navigation across diverse and unpredictable environments. By integrating normalization techniques with the Reinforce algorithm, she demonstrates how Jetbot platforms can overcome limitations posed by varying weather, terrain, and unknown parameters—achieving robust, generalized path-following without extensive retraining. This contribution has already garnered 2 citations, signaling its relevance to the field. Subhangi’s research bridges the gap between theoretical reinforcement learning and practical robotic deployment, offering scalable solutions for autonomous systems. Her work is particularly notable for its emphasis on real-world applicability, tackling issues like environmental variability that often hinder traditional navigation methods. As a researcher, she is positioned at the intersection of machine learning and robotics, with potential for significant impact in areas such as self-driving vehicles, drone navigation, and industrial automation. Her findings provide a foundation for future studies in adaptive, learning-based control systems.
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Top Papers
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