Debarghya Das
Papers
2
Total Citations
29
H-Index
2
About
Debarghya Das is a researcher whose work sits at the intersection of robotics, crowdsourcing, and human-robot interaction, with a particular focus on learning user preferences to improve autonomous navigation. His most notable contribution is the development of **PlanIt**, a novel crowdsourcing framework that learns to plan optimal robot paths from large-scale preference feedback. This work addresses a fundamental challenge in robotics: the definition of a "good" trajectory is highly subjective, varying with individual users, specific tasks, and dynamic environmental contexts. By representing these trajectory preferences using a cost function learned from crowd-sourced comparisons, Das’s research enables robots to adapt their movement to human expectations, making autonomous systems more intuitive and socially aware. His flagship paper on PlanIt has accumulated **26 citations**, demonstrating its influence in the field. This work is particularly significant for students and researchers interested in scalable methods for teaching robots complex, context-dependent behaviors without explicit programming. Das’s research provides a practical bridge between machine learning, human factors, and real-world robotic deployment, offering a compelling approach to making robots better collaborators in human-centric environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2