Thirulogasankar Pranav Kutralingam
Papers
1
Total Citations
3
H-Index
1
About
Thirulogasankar Pranav Kutralingam is a rising researcher at the forefront of deep reinforcement learning (RL), with a sharp focus on bridging the critical gap between simulated training environments and real-world deployment. His most cited work, a comprehensive 2025 survey on sim-to-real methods in RL, has already garnered early attention for its timely synthesis of progress, persistent challenges, and the transformative potential of foundation models in this domain. Kutralingam’s research systematically addresses how RL agents, proven effective in decision-making tasks across robotics, transportation, and recommender systems, can reliably transfer policies learned in simulation to unpredictable physical settings. By cataloging advances and outlining a roadmap for integrating large-scale pretrained models, his survey serves as a vital resource for researchers tackling the sim-to-real bottleneck. Though early in his career, Kutralingam’s work signals a deep commitment to making RL more robust and practically deployable. His contributions are particularly valuable for students and engineers seeking to understand where the field stands and where it is headed, marking him as a thoughtful voice in the evolution of reinforcement learning toward real-world impact.
Research Focus
Key Achievements
Top Papers
- 1