Sruthy Suresh
Papers
1
Total Citations
13
H-Index
1
About
Sruthy Suresh is pushing the boundaries of decentralized intelligence in multi-robot systems, with a primary focus on federated reinforcement learning. Her most-cited work, "On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios" (2022, 13 citations), tackles a critical bottleneck in modern robotics: how to enable collaborative learning across robots without relying on a central server or cloud. By removing this single point of failure, Suresh’s research enhances both privacy and bandwidth efficiency, allowing robots to share learned policies while keeping their raw data local. This contribution is especially vital for real-world deployments in search-and-rescue, warehouse automation, and environmental monitoring, where connectivity is unreliable and data sensitivity is paramount. Her work bridges the gap between theoretical federated learning frameworks and practical, scalable multi-robot coordination. As a rising voice in the field, Suresh is helping to shape a future where robot teams can learn and adapt collectively, securely, and autonomously—without needing to phone home.
Research Focus
Key Achievements
Top Papers
- 1On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios13 citations · 2022