Raghav Nagpal
Papers
1
Total Citations
7
H-Index
1
About
Raghav Nagpal’s research lies at the intersection of robotics and reinforcement learning, with a particular focus on reward engineering for complex manipulation tasks. His most-cited work, “Reward Engineering for Object Pick and Place Training” (2020, 7 citations), addresses a critical challenge in robotic grasping: designing reward functions that enable agents to learn efficient, transferable policies for pick-and-place operations. By systematically exploring how reward shaping affects learning speed and task success, Nagpal’s contributions help accelerate automation across industries from manufacturing to healthcare. His approach emphasizes practical, real-world applicability, bridging the gap between theoretical RL algorithms and deployable robotic systems. While early in his career, Nagpal’s work has already informed subsequent studies on reward design in manipulation, and his focus on object interaction training positions him as a promising voice in the growing field of robot learning. His research is particularly valuable for students and engineers seeking to understand how careful reward specification can unlock robust, autonomous behavior in physical agents.
Research Focus
Key Achievements
Top Papers
- 1Reward Engineering for Object Pick and Place Training7 citations · 2020