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About

Maziar Palhang is a robotics and artificial intelligence researcher whose work spans autonomous navigation, simultaneous localization and mapping (SLAM), multi-agent systems, and reinforcement learning. His research addresses fundamental challenges in enabling robots to perceive, navigate, and make decisions in complex, real-world environments. Among his most notable contributions is the development of POMCP-based decentralized spatial task allocation algorithms for partially observable environments, which has garnered 7 citations and advances how autonomous agents coordinate in uncertain settings. His innovative funnel lane concept for qualitative vision-based navigation offers a distinctive approach to robot positioning using visual cues, accumulating 6 citations across related publications. Palhang has also made meaningful contributions to SLAM methodology, investigating both relative and absolute map filtering approaches and their performance across varied path configurations. More recently, his work on humanoid robot balance control using deep reinforcement learning demonstrates his expanding interest in hierarchical control architectures combining actor-critic neural networks. His 2021 work on Layered Relative Entropy Policy Search further reflects his commitment to advancing principled learning algorithms for robotic systems. Collectively, Palhang's research represents a cohesive effort to bridge perception, localization, and intelligent decision-making in autonomous robotics.