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About

Zhenmin Tang is a pioneering researcher in embodied artificial intelligence and multi-robot systems, whose work bridges perception, navigation, and cooperative autonomy. His key research areas include embodied question answering (EQA), dynamic obstacle avoidance, multi-robot service-oriented architectures, and bio-inspired robotics. Tang’s most influential contribution is SegEQA (2019, 30 citations), which introduced video segmentation-based visual attention for embodied question answering—a critical advance for autonomous driving and in-home robots. He also developed the Collision Time Histogram (CTH) algorithm (2017, 26 citations), a novel approach to dynamic obstacle avoidance for unmanned ground vehicles. His work on multi-robot service-oriented architecture (2016, 22 citations) addresses heterogeneity in robot teams, proposing a layered model for cooperative behavior and energy-aware service provision. Tang’s bat-like switched flying and adhesive robot (2012, 14 citations) demonstrates his versatility, achieving low-power wall adhesion for aerial robots. More recently, he has tackled perception attacks in embodied AI with a deepfake detection model (2024, 10 citations). With foundational contributions to LiDAR scan-matching (2009, 24 citations) and visibility-based boundary coverage (2008), Tang’s research has accumulated over 150 citations, shaping modern autonomous systems from path tracking to team evolution.