Ramya Ramakrishnan

Massachusetts Institute of Technology, American Institute of Aeronautics and Astronautics

Papers

Total Citations

287

H-Index

About

Ramya Ramakrishnan is a researcher at the forefront of human-robot interaction and collaborative artificial intelligence, with a particular focus on enabling robots to work seamlessly alongside human partners. Her most influential contribution, "Efficient Model Learning from Joint-Action Demonstrations for Human-Robot Collaborative Tasks" (2016, 183 citations), introduced a groundbreaking framework that allows robots to automatically learn human behavioral models from demonstrations, clustering user behavior to compute robust collaborative policies. This work fundamentally advanced how robots can adapt to individual human partners in real-world settings. Ramakrishnan has also made significant strides in translating human team-training methodologies into human-robot contexts. Her 2015 paper on cross-training (87 citations) demonstrated that having humans and robots iteratively switch roles dramatically improves team performance — a creative bridge between organizational psychology and robotics. Building on this, her perturbation training research (2017) further explored co-developed joint strategies for novel coordination tasks. Together, her body of work represents a compelling vision: that robots need not merely follow instructions, but can become genuine collaborative partners by learning from, and training alongside, the humans they work with.