Lekha Mohan

Papers

2

Total Citations

38

H-Index

2

About

Lekha Mohan is a robotics researcher whose work bridges the gap between data-driven imitation learning and practical, real-world manipulation. Her key research areas include robot manipulation, imitation learning, and human-robot interaction, with a focus on scaling robotic learning beyond single-task simulations. Her most impactful contribution is the "Multiple Interactions Made Easy (MIME)" dataset, which provides large-scale demonstrations for robotic manipulation—a pioneering effort that has garnered 36 citations and addresses the critical need for diverse, real-world training data. This work stands out for enabling robots to learn from multiple interaction types, moving beyond isolated tasks like grasping or pushing. Mohan also contributed to humanitarian robotics with her design of a borewell rescue robot, demonstrating her commitment to applying robotics for social good. Her research has been recognized for its potential to advance the field of imitation learning, making her a notable figure in the push toward more versatile, data-efficient robotic systems.

Research Focus

Key Achievements

2
H-Index
2
Papers
38
Total Citations
19
Avg Citations/Paper
🏆 Most Cited Paper
Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitation
36 citations · 2018
📈 Most Prolific Year: 2018 (1 Papers)
🤝 Key Collaborators: 7

Top Papers

  1. 1
  2. 2

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago