M. P. Pramuk
Papers
1
Total Citations
2
H-Index
1
About
Dr. M. P. Pramuk is an emerging leader in the field of multi-robot systems and autonomous navigation, with a primary focus on integrating advanced machine learning techniques to solve complex coordination challenges. Their most notable contribution is the development of a novel Federated Deep Reinforcement Learning (FDRL) framework, which leverages the Twin Delayed Deep Deterministic Policy Gradients (TD3) algorithm to enable goal-driven, autonomous movement in multi-robot teams. This work, published in 2024, directly addresses critical issues in scalability and data privacy within multi-robot systems by allowing robots to learn collaborative navigation policies without sharing raw data. Although early in its citation impact, this research represents a significant step forward in creating more robust and efficient distributed robotic swarms. Dr. Pramuk’s work is particularly relevant for applications in search-and-rescue, environmental monitoring, and automated logistics, where coordinated, decentralized decision-making is essential. By bridging the gap between reinforcement learning and real-world robotic constraints, Dr. Pramuk is establishing a promising research trajectory that will likely influence future developments in autonomous systems and intelligent robotics.
Research Focus
Key Achievements
Top Papers
- 1