Papers
125
Total Citations
2,327
H-Index
24
About
Dan O. Popa is a pioneering robotics researcher whose work spans human-robot interaction, adaptive control, micro/nanoscale assembly, and autonomous systems. Perhaps best known for his highly influential 2015 paper on reinforcement learning-based assistive human-robot interaction (219 citations), Popa has consistently pushed the boundaries of how robots perceive, adapt to, and collaborate with humans. His contributions to physical human-robot interaction (pHRI) are particularly notable, with landmark studies on adaptive admittance control (122 citations) and model-free neuroadaptive controllers (73 citations) that enable robots to safely and intelligently respond to human intent in real time. Beyond interaction, Popa has made foundational contributions to micro and mesoscale robotic assembly (126 citations), developing multiscale manufacturing systems capable of constructing complex micro-nano devices — work with significant implications for MEMS and microsystems commercialization. His early research on adaptive force and impedance control (103 citations) laid important theoretical groundwork for modern compliant robotics. Popa has also advanced autonomous underwater vehicle sampling strategies and robotic sensor network deployment. More recently, his 2020 clinical study on robotic nursing assistants (53 citations) underscores his commitment to translating research into meaningful real-world healthcare applications.
Research Focus
Key Achievements
Top Papers
- 1Optimized Assistive Human–Robot Interaction Using Reinforcement Learning219 citations · 2015
- 2Micro and Mesoscale Robotic Assembly126 citations · 2004
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- 5Adaptive sampling algorithms for multiple autonomous underwater vehicles84 citations · 2004
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- 9Robotic deployment of sensor networks using potential fields72 citations · 2004
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