Papers
5
Total Citations
97
H-Index
3
About
Amit Surana’s research lies at the intersection of human-robot interaction, control systems, and multi-agent coordination, with a strong emphasis on designing intelligent, human-supervised autonomous teams. His major contributions include developing optimization frameworks for adaptive attention allocation—determining how and where a human operator should focus their monitoring in human-robot systems—and advancing human supervisory control by integrating cognitive modeling with engineering design. His most-cited work, “Human Supervisory Control of Robotic Teams” (2015, 63 citations), is a foundational reference in the control systems community, addressing how operators oversee autonomous agents using sensor feedback. Surana has also tackled distributed map fusion for large-scale SLAM, enabling robot teams to build maps in parallel and fuse them efficiently. More recently, his work on reinforcement learning from an industrial perspective (2021) signals a growing interest in applying RL to real-world manufacturing and logistics. With a career spanning theoretical planning under process algebraic constraints to practical multi-vehicle routing, Surana’s research is characterized by its rigor and relevance to fielded autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Adaptive attention allocation in human-robot systems21 citations · 2012
- 3Distributed map fusion with sporadic updates for large domains8 citations · 2015
- 4Reinforcement Learning: An Industrial Perspective3 citations · 2021
- 5