Sabita Pal
Papers
2
Total Citations
8
H-Index
2
About
Sabita Pal is a researcher whose work sits at the intersection of reinforcement learning and autonomous robotics, with a particular focus on optimizing machine intelligence for real-world decision-making. Her most notable contribution is the development of a novel framework that examines the coupling effect between exploration rate and learning rate in reinforcement learning, leading to optimized scaled learning algorithms that improve how autonomous systems adapt and make decisions in complex environments. This work, published in 2023, has already garnered attention with 4 citations. Pal also co-authored a comprehensive review on the evolution of Simultaneous Localization and Mapping (SLAM) frameworks for autonomous robotics, which has equally earned 4 citations. This review critically analyzes how machine intelligence shapes decision-making in operational state-spaces, addressing the growing demand for seamless human–machine interaction in industrial settings. Her research is particularly relevant for students and engineers working on autonomous navigation, adaptive control systems, and reinforcement learning optimization. By bridging theoretical insights with practical robotics challenges, Pal is contributing to the foundational knowledge that will drive more intelligent, efficient, and responsive autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 2