Vaibhav V. Unhelkar

PhD Candidate
Interactive Robotics Group
AeroAstro · CSAIL

32-314, Stata Center

GitHub · LinkedIn
ResearchGate · Google Scholar

I am a PhD candidate in the Interactive Robotics Group(IRG) at Massachusetts Institute of Technology advised by Prof. Julie A. Shah. My research focuses on developing decision making algorithms for robots that interact with humans. For developing these algorithms, I use tools from planning, inference, machine learning, and insights from human factors.

During my academic training, I have worked on several autonomous systems: satellites, aerial vehicles and mobile robots. As part of my M.S. research at MIT, I worked on developing a mobile robotic assistant for automotive final assembly lines. Prior to joining IRG, I was a researcher with the Dynamics and Controls group at the Department of Aerospace Engineering, IIT Bombay, where my work focused on System Identification of miniature aerial vehicles.

I obtained my undergraduate education at the Indian Institute of Technology (IIT) Bombay through the integrated B.Tech. + M.Tech. programme in Aerospace Engineering. My Master's and Bachelor's theses focused on spacecraft attitude estimation and GPS-INS integration, respectively. At IIT Bombay, I was a member of the Pratham student satellite team. As a summer intern, I have also worked for Turbomeca on the problem of volcanic ash in helicopter engines.

I am passionate about education and have been involved in educational outreach and mentoring activities. Along with research and education, I cherish interesting conversations, the works of Isaac Asimov, and instrumental music.


Jun '15
I completed my M.S. at the Interactive Robotics Group, MIT. My master's thesis is titled "Towards a Mobile Robotic Assistant for Automotive Final Assembly Lines: Control, Sensing and Human Robot Interaction". Special thanks to my collaborators for making the project a success!
May '15
I presented our paper on Human-Robot Co-Navigation at the International Conference on Robotics and Automation, Seattle, WA. Through a study of human walking motion we confirmed the existence of anticipatory indicators of human walking motion. Further, we used this insight for prediction of human motion using time-series classification. [Details]
Jan '15
Amidst Boston's seemingly unending snow, completed my qualifier exams. Now a PhD Candidate!