Shadi Abpeikar
UNSW Sydney, Camden and Campbelltown Hospitals, University of Canberra, UNSW Canberra
Papers
11
Total Citations
94
H-Index
6
About
Shadi Abpeikar is a robotics and artificial intelligence researcher specializing in swarm robotics, collective motion, and multi-robot systems. His work sits at the compelling intersection of bio-inspired algorithms, deep reinforcement learning, and autonomous robot behavior, drawing inspiration from natural phenomena such as bird flocking and fish schooling to engineer sophisticated robotic systems. Among his most significant contributions is the development of frontier-led swarming, a robust framework enabling multi-robot teams to efficiently cover unknown environments, which has garnered 33 citations since its 2022 publication and represents a meaningful advance in autonomous exploration. Abpeikar has also pioneered techniques for automatically tuning collective motion behaviors, eliminating the need for laborious hand-tuning through actor-critic reinforcement learning and transfer learning approaches — work that bridges the gap between simulation and real-world deployment across multiple robot platforms. His research on autonomously recognizing emergent collective behaviors using behavioral metrics and deep learning further demonstrates his breadth, offering practical tools for evaluating and controlling swarm dynamics. With over 90 cumulative citations across his portfolio and ongoing work tackling obstacle-rich environments and domain transfer, Abpeikar is establishing himself as an emerging voice in scalable, intelligent multi-robot systems.
Research Focus
Key Achievements
Top Papers
- 1Frontier-led swarming: Robust multi-robot coverage of unknown environments33 citations · 2022
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- 6Transfer Learning for Autonomous Recognition of Swarm Behaviour in UGVs6 citations · 2022
- 7Autonomous Recognition of Collective Behaviour in Robot Swarms5 citations · 2020
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