Papers
2
Total Citations
5
H-Index
2
About
Kratarth Goel’s research focuses on advancing autonomous systems, particularly in motion forecasting and robot navigation. His major contributions include pioneering methods to enhance real-time motion forecasting under constrained onboard compute budgets, as demonstrated in his 2024 work “Scaling Motion Forecasting Models with Ensemble Distillation.” This paper proposes ensemble distillation techniques to improve accuracy without exceeding computational limits, a critical challenge for autonomous vehicles and robotics. Additionally, his 2013 study “Autonomous Robot Navigation: Path Planning on a Detail-Preserving Reduced-Complexity Representation of 3D Point Clouds” introduces efficient path planning by simplifying 3D point cloud data while retaining essential details, enabling faster and more reliable navigation. Though early in his career, Goel’s work has already garnered citations, signaling growing impact in the field. His innovative approaches to balancing performance and efficiency in autonomous systems hold promise for real-world applications, from self-driving cars to robotic exploration.
Research Focus
Key Achievements
Top Papers
- 1Scaling Motion Forecasting Models with Ensemble Distillation3 citations · 2024
- 2