Armin Avaei
Papers
1
Total Citations
11
H-Index
1
About
Armin Avaei is a robotics researcher whose work lies at the intersection of motion planning, human-robot interaction, and machine learning. His primary research focus is on developing algorithms that enable robots to understand and adapt to human preferences, making autonomous systems more intuitive and collaborative. In his most-cited work, "An Incremental Inverse Reinforcement Learning Approach for Motion Planning with Separated Path and Velocity Preferences" (2023, 11 citations), Avaei tackles the challenge of integrating both spatial and temporal human preferences—such as personal safety margins or execution styles—into robotic trajectory planning. This approach allows robots to learn from human demonstrations incrementally, improving their ability to generate socially compliant and personalized motions. By separating path and velocity preferences, his work addresses a critical gap in inverse reinforcement learning, offering a more nuanced framework for modeling diverse human behaviors. Avaei's contributions are particularly impactful for applications in autonomous driving, assistive robotics, and industrial automation, where robots must operate safely alongside humans. His research advances the frontier of learning from demonstration, paving the way for more adaptable and human-aware robotic systems.
Research Focus
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Top Papers
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