Papers
15
Total Citations
266
H-Index
9
About
Payam Ghassemi is a robotics and autonomous systems researcher whose work spans multi-robot task allocation, swarm robotics, and human-swarm interaction, with a particular focus on real-world disaster response applications. His most influential contribution, "Multi-robot task allocation in disaster response" (2021, 76 citations), addresses the complex challenge of coordinating robot teams under dynamic, time-sensitive conditions with practical hardware constraints. Building on this foundation, Ghassemi has pioneered the application of graph-based deep reinforcement learning — notably through Capsule Attention Networks — to develop scalable, generalizable policies for multi-robot coordination, earning 34 citations for that work alone. His research into decentralized swarm systems reflects a consistent commitment to fault-tolerant, scalable architectures suitable for real-world deployment, with multiple papers advancing Bayesian optimization approaches for swarm search and informative path planning. Ghassemi has also explored the human side of autonomy, examining how physiological states influence decision-making in human-swarm teams. Early work on humanoid robot balance control and UAV ergonomics in warehouse environments further demonstrates his breadth. With over 200 cumulative citations, Ghassemi has established himself as a significant voice in intelligent multi-robot systems research.
Research Focus
Key Achievements
Top Papers
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- 4Push recovery for NAO humanoid robot19 citations · 2014
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- 7Learning Robot Swarm Tactics over Complex Adversarial Environments14 citations · 2021
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