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About

Minjie Hua is a researcher at the forefront of autonomous robotics and human-robot interaction, with a focus on enabling robots to navigate and communicate with greater intelligence and realism. His key research areas include computer vision, semantic segmentation, and natural language-driven gesture generation. Hua’s major contribution is the development of a small obstacle avoidance system for road robots using RGB-D semantic segmentation, a critical innovation that allows autonomous platforms to detect and circumvent hazards often missed by conventional sensors. This work, which has garnered 31 citations, directly addresses a fundamental challenge in real-world autonomous navigation. In parallel, Hua has advanced human-robot communication by designing a Seq2Seq-based body gesture interaction system, which enables robots to exhibit more natural, context-aware gestures during conversation. This system, with 11 citations, moves beyond simple verbal exchanges to create more immersive and realistic interactions. By bridging the gap between perception and social behavior, Minjie Hua’s research is paving the way for safer, more intuitive robots capable of operating seamlessly alongside humans in dynamic environments.