Rohan Choudhury

About

Rohan Choudhury’s research lies at the intersection of robotics and human-robot interaction (HRI), with a sharp focus on how machines learn from and adapt to human behavior. His most influential work, “On the Utility of Model Learning in HRI” (2019), has garnered 42 citations, establishing him as a key voice in the foundational debate between model-based and model-free learning. Choudhury argues that in HRI, the human is not merely an environmental variable but a complex, dynamic agent—and the choice between building an explicit model of that agent or treating it as a black box has profound implications for robot performance, safety, and trust. By dissecting when and why model learning is beneficial, he provides a principled framework for designing robots that can anticipate human actions rather than simply react. His contributions help bridge the gap between theoretical reinforcement learning and real-world collaborative systems. For students and researchers, Choudhury’s work is a must-read for anyone grappling with how robots should represent the people they work alongside—a question that remains central to the future of autonomous, socially aware machines.

Research Focus

Key Achievements

Top Papers

1. 1
   On the Utility of Model Learning in HRI

   42 citations · 2019
2. 2
   On the Utility of Model Learning in HRI

   11 citations · 2019

Key Collaborators

Contact & Links