Shaohui Liu

Harbin Institute of Technology, ETH Zurich

Papers

Total Citations

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About

Shaohui Liu is a researcher at the forefront of human-robot collaboration and 3D scene understanding, whose work bridges the gap between adaptive robotics and advanced computer vision. His key research areas include learning from demonstration, human-robot interaction in assembly tasks, and neural implicit representations for robotics. Liu’s major contribution lies in developing prediction-based collaboration models that enable robots to anticipate human actions, significantly improving workflow efficiency in small-to-medium enterprises. His 2022 paper on this topic, which has garnered 34 citations, demonstrates a practical framework for seamless human-robot teamwork. Additionally, his early work on depth estimation from single monocular images using deep hybrid networks (2016, 18 citations) laid groundwork for efficient 3D perception. More recently, Liu has been exploring the integration of Neural Radiance Fields (NeRFs) into robotics, as evidenced by his 2024-2025 survey papers that consolidate advances in neural implicit representations for realistic environment modeling. With a growing citation impact and a focus on real-world applications, Liu is shaping the future of intelligent, collaborative robotic systems.