Sanjana Sharma
Papers
1
Total Citations
12
H-Index
1
About
Sanjana Sharma is a leading voice in human-robot interaction, with a research focus on how humans naturally learn to understand and collaborate with robotic systems. Her most-cited work, "Revisiting Human-Robot Teaching and Learning Through the Lens of Human Concept Learning" (2022), fundamentally reframes robot transparency by drawing on cognitive science. Sharma demonstrates that humans automatically form conceptual models of a robot’s behavior—often unconsciously—simply by watching or interacting with it. Her key contribution lies in identifying that these mental models vary in quality and can be actively shaped by how robot behaviors are selected and presented. This insight has direct implications for designing more intuitive human-robot teaching interfaces, making her work essential reading for researchers in interactive AI and cognitive robotics. With 12 citations on this paper alone, Sharma’s work is gaining traction as a foundational perspective in the field. Her research bridges machine learning and human cognition, offering a principled way to improve human-robot teaming by aligning robot demonstrations with how people actually learn.
Research Focus
Key Achievements
Top Papers
- 1