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About

Marco Aiello is an emerging researcher at the intersection of robotics, artificial intelligence, and human-robot interaction, with a particular focus on long-term mobile robot task planning in dynamic, human-populated environments. His work addresses one of the field's most pressing challenges: enabling robots to operate intelligently and safely alongside people in real-world settings. Aiello's most influential contribution, "Human-Flow-Aware Long-Term Mobile Robot Task Planning Based on Hierarchical Reinforcement Learning" (2023, 16 citations), introduced a novel framework that explicitly incorporates human movement patterns into robot planning — a significant step beyond conventional approaches that ignore social dynamics. Building on this foundation, his DELTA framework (2024–2025, accumulating 15 citations across versions) leverages Large Language Models to decompose and solve complex, long-horizon robot planning tasks with remarkable efficiency and context awareness. More recently, Aiello has turned his attention to multimodal LLMs for human behavior prediction, exploring how generative AI can transcend the limitations of domain-specific training data. His work on telepresence robot automation further demonstrates his breadth across perception, tracking, and autonomous navigation. Together, his contributions position him as a promising voice in the push toward socially intelligent, adaptable robotic systems.