Soumya Ranjan Sahoo
Papers
2
Total Citations
3
H-Index
1
About
Soumya Ranjan Sahoo is an emerging researcher working at the intersection of robotics, control systems, and deep reinforcement learning. His work focuses on developing intelligent control strategies for robotic manipulators, with a particular emphasis on leveraging advanced machine learning techniques to solve complex real-world control challenges. Sahoo's most notable contributions center on applying Deep Deterministic Policy Gradient (DDPG) algorithms to robotic control problems. His 2023 paper introduced an adaptive robust controller built upon an improved DDPG framework, earning 2 citations and demonstrating early recognition within the robotics and reinforcement learning communities. Building on this foundation, his 2024 work tackled the demanding problem of three-dimensional trajectory tracking for robotic manipulators, innovating through sequential deep reinforcement learning to dramatically accelerate training while incorporating disturbance rejection capabilities — a critical requirement for robust real-world deployment. Though still early in his research career, Sahoo's focus on model-free learning approaches that can handle dynamic, uncertain environments positions him as a promising contributor to the growing field of intelligent robotics. Students and researchers interested in reinforcement learning applications for autonomous robotic control will find his evolving body of work a valuable and practically oriented reference.
Research Focus
Key Achievements
Top Papers
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- 2