Papers
6
Total Citations
48
H-Index
4
About
Rahul Rai’s research sits at the intersection of human-robot interaction, autonomous systems, and machine learning for engineering design. His work addresses fundamental challenges in enabling machines to understand, predict, and collaborate with humans in complex environments. Rai’s most cited paper, “PIMNet: Physics-Infused Neural Network for Human Motion Prediction” (2022, 26 citations), introduces a novel approach that integrates physical constraints into deep learning models, significantly improving the accuracy and plausibility of predicted human poses—a critical capability for robots operating alongside people. He has also made notable contributions to multi-operator task allocation in human-robot teams (2013, 9 citations) and to autonomous navigation for agricultural tractor-trailer systems (2025, 6 citations), demonstrating the breadth of his applied work. Rai’s research on wrench uncertainty quantification in cable robots (2017, 4 citations) further showcases his ability to tackle uncertainty in loosely interconnected cooperative systems. As editor of special issues on machine learning in CAD/CAM (2023) and symbiotic human-AI partnerships for next-generation factories (2022), he actively shapes discourse on integrating AI into engineering practice. His work consistently bridges theoretical modeling with real-world deployment, making him a leading voice in creating intelligent, collaborative systems for manufacturing and robotics.
Research Focus
Key Achievements
Top Papers
- 1PIMNet: Physics-Infused Neural Network for Human Motion Prediction26 citations · 2022
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- 5Special Issue: Machine Learning and Representation Issues in CAD/CAM2 citations · 2023
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