Daniel T. Gamarra
Papers
1
Total Citations
4
H-Index
1
About
Daniel T. Gamarra is a robotics researcher whose work centers on deep reinforcement learning (DRL) for autonomous navigation, with a particular focus on bridging the gap between simulation and real-world deployment. His major contribution lies in developing algorithms that improve generalization in mobile robot control—both aerial and terrestrial—by addressing the critical challenge of delayed policy learning. This approach enables robots to adapt more robustly to dynamic, unstructured environments without relying on pre-built maps. His most-cited paper, "Improving Generalization in Aerial and Terrestrial Mobile Robots Control Through Delayed Policy Learning" (2024), has already garnered early attention with 4 citations, signaling growing impact in the field. Gamarra’s work is notable for its practical implications: by enhancing sample efficiency and policy transferability, his methods reduce the costly trial-and-error often required in real-world robotics. As a rising researcher, he is helping to push DRL-based control toward safer, more reliable autonomy—a key step for applications in search-and-rescue, surveillance, and beyond.
Research Focus
Key Achievements
Top Papers
- 1