Papers
19
Total Citations
720
H-Index
10
About
Vaibhav Unhelkar is a robotics and human-robot interaction researcher whose work sits at the intersection of autonomous systems, collaborative robotics, and human-aware AI. His research has made foundational contributions to deploying mobile robots in real-world manufacturing environments, particularly automotive assembly lines — a notoriously complex domain where robots must seamlessly coexist with human workers. His early work demonstrated that mobile robotic assistants could perform fetch-and-deliver tasks comparably to human counterparts, while subsequent research tackled the formidable challenges of navigating dynamic, human-occupied spaces using anticipatory motion prediction techniques. Unhelkar's investigations into trust dynamics in automation — his most-cited work with 195 citations — helped reshape how the field understands human-robot trust as an evolving rather than static phenomenon. His 2018 human-aware robotic assembly assistant, cited nearly 150 times, integrated motion prediction with planning to enable genuinely safe and efficient collaboration. Beyond physical interaction, Unhelkar has explored intelligent communication frameworks that determine when and what robots should convey during collaborative tasks, as well as semi-supervised learning and reward-shaping methods for contact-rich manipulation. Collectively, his portfolio reflects a commitment to making collaborative robots practically deployable, trustworthy, and socially intelligent partners in human-centered workplaces.
Research Focus
Key Achievements
Top Papers
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- 8Learning Dense Rewards for Contact-Rich Manipulation Tasks24 citations · 2021
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