Mingxiao Huo

Papers

Total Citations

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About

Mingxiao Huo is a researcher focused on the intersection of artificial intelligence and robotics, with a particular emphasis on human-oriented representation learning for robotic manipulation. Their work aims to bridge the gap between human cognitive models and machine learning algorithms, enabling robots to more intuitively understand and execute complex manipulation tasks. Huo’s most-cited paper, "Human-oriented Representation Learning for Robotic Manipulation" (2024), introduces novel frameworks that leverage human demonstrations and perceptual cues to improve robot dexterity and adaptability in real-world environments. Although early in their career, this work has already garnered attention, accumulating 2 citations and signaling growing interest in their approach. Huo’s contributions are particularly notable for their potential to enhance human-robot collaboration, making robotic systems more accessible and efficient in settings ranging from manufacturing to assistive technology. By prioritizing human-centered design, Huo is helping to shape a future where robots can learn from and work alongside people with greater ease and precision.