Ramya Muthukrishnan
Papers
1
Total Citations
2
H-Index
1
About
Ramya Muthukrishnan is a rising researcher at the intersection of robotics, multi-agent systems, and machine learning. Her work focuses on decentralized coordination and control for robot swarms, particularly in environments where agents must learn to communicate and adapt without centralized oversight. In her highly cited paper, “LPAC: Learnable Perception-Action-Communication Loops With Applications to Coverage Control,” she introduces a novel framework that enables robot teams to collaboratively monitor unknown phenomena by learning both perception and communication policies. This work addresses the fundamental challenge of coverage control—where agents must dynamically allocate themselves to monitor features of interest—in a fully decentralized setting. By integrating differentiable communication channels with reinforcement learning, Muthukrishnan’s approach allows swarms to autonomously develop efficient coordination strategies. Her contributions are significant for applications ranging from environmental monitoring to search-and-rescue operations. With early citations already accruing to her 2025 paper, Muthukrishnan is establishing herself as a key voice in the growing field of learnable multi-robot systems, pushing the boundaries of how intelligent agents can collaborate in real-world, unknown environments.
Research Focus
Key Achievements
Top Papers
- 1