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About

Jieliang Luo is a robotics and machine learning researcher whose work sits at the intersection of robotic assembly, reinforcement learning, and intelligent automation. His research focuses on enabling robots to perform complex, contact-rich manipulation tasks with high precision — challenges that are central to modern manufacturing and industrial automation. Luo's most prominent contribution is ASAP (2024, 18 citations), a physics-based planning system that automatically generates feasible assembly sequences for complex, multi-part products — a significant step toward fully autonomous robotic manufacturing. Complementing this, his force-guided assembly work (2021, 14 citations) introduced a robot-agnostic learning approach that leverages force feedback rather than vision, allowing robotic systems to handle high-precision assembly across different hardware platforms with greater adaptability. On the learning side, Luo developed Dynamic Experience Replay (2020, 9 citations), a technique that enhances reinforcement learning efficiency by incorporating both human demonstrations and agent-generated transitions into training. He has also explored reward shaping through self-supervised temporal signals and ventured into creative applications of RL for generative art, demonstrating a broad intellectual curiosity. With over 40 cumulative citations, Luo's work is steadily shaping the future of intelligent robotic systems.