Heng Xiong

Huazhong University of Science and Technology

Papers

Total Citations

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About

Heng Xiong is a researcher specializing in robotic automation, deep reinforcement learning (DRL), and combinatorial optimization, with a particular focus on the challenging domain of 3D Bin Packing Problems (3D-BPP). His work sits at the intersection of artificial intelligence and practical robotics, addressing real-world logistics and manufacturing challenges where efficient spatial reasoning is critical. Xiong's most impactful contribution, "Towards Reliable Robot Packing System Based on Deep Reinforcement Learning" (2023), has garnered 37 citations, establishing him as a meaningful voice in the field of intelligent robotic packing systems. His follow-up work, GOPT (2024), advances the state of the art by introducing transformer-based DRL architectures that prioritize generalizability across diverse packing environments — a limitation that had constrained earlier approaches. This emphasis on robust, adaptable solutions reflects a broader ambition to bridge the gap between controlled research settings and real-world deployment. Through comparative studies examining conventional heuristic methods alongside DRL approaches, Xiong brings analytical rigor to a rapidly evolving field. His research collectively contributes to making autonomous robotic packing systems more reliable, generalizable, and practically deployable across the global logistics and automation industry.