Ishikaa Lunawat
Papers
2
Total Citations
14
H-Index
2
About
Ishikaa Lunawat is a rising robotics researcher whose work centers on advancing robotic manipulation through novel simulation and perception techniques. Her primary research areas include 6DoF robotic grasping, neural rendering for manipulation, and simulation-based policy evaluation. Lunawat’s most impactful contribution is NeuGraspNet, a groundbreaking method that reinterprets robotic grasping as a neural rendering problem, enabling effective 6DoF grasping of objects in cluttered scenes from any single viewpoint without requiring additional scene exploration—a significant leap for real-world deployment. This work has already garnered 10 citations since its 2024 publication. She has also made notable strides in addressing the scalability and reproducibility crisis in robotics research, developing simulation frameworks for evaluating real-world robot manipulation policies that promise to accelerate progress while reducing costs. Her contributions are particularly valuable as the field moves toward generalist manipulation policies capable of performing an ever-widening spectrum of tasks. Lunawat’s research sits at the critical intersection of perception, learning, and physical interaction, offering practical solutions that bridge the gap between simulated training and real-world application.
Research Focus
Key Achievements
Top Papers
- 1
- 2Evaluating Real-World Robot Manipulation Policies in Simulation4 citations · 2024