Papers
1
Total Citations
2
H-Index
1
About
Aarav Nigam is a rising researcher at the intersection of robotics, reinforcement learning, and autonomous navigation. His work centers on developing adaptive, learning-based solutions for real-world robotic control, with a particular focus on overcoming the fragility of traditional path-following algorithms in dynamic environments. His most-cited paper, "Generalized Visual Path Following on Jetbot Using Normalization with Reinforcement Learning" (2024), directly tackles this challenge by applying the REINFORCE algorithm to enable a low-cost JetBot to generalize across varying terrains, lighting, and weather conditions without manual recalibration. This contribution is significant for its practical, scalable approach to robust autonomy, demonstrating how normalization techniques can stabilize learning in vision-based control tasks. With early citations already recognizing its potential, Nigam’s work is gaining traction in the robotics community. His research is especially relevant for students and engineers interested in bridging the gap between simulation and real-world deployment, offering a clear pathway from foundational reinforcement learning concepts to applied robotic systems.
Research Focus
Key Achievements
Top Papers
- 1