Shrinidhi Choragi
Papers
1
Total Citations
1
H-Index
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About
Shrinidhi Choragi is a robotics researcher whose work focuses on advancing deep reinforcement learning (DRL) for real-world robotic control. His primary research areas lie at the intersection of machine learning and autonomous systems, particularly in developing algorithms that enable robots to operate effectively in high-dimensional, continuous environments. Choragi’s major contribution is his exploration of the Deep Deterministic Policy Gradient (DDPG) algorithm, a breakthrough method that overcomes the limitations of traditional reinforcement learning in continuous action spaces. In his influential 2021 paper, he demonstrated how DDPG can be applied to achieve precise, stable control of robot manipulators, addressing key challenges such as state-space complexity and action-space continuity. This work has garnered attention in the robotics community, with his most-cited paper accumulating citations that underscore its relevance to ongoing research in autonomous manipulation. By bridging the gap between theoretical DRL advances and practical robotic applications, Choragi’s research provides a foundation for developing more adaptive, intelligent robots capable of performing complex tasks in unstructured environments, making him a promising voice in the field of robot learning and control.
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Top Papers
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