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About

Xixing Li is a rising researcher whose work bridges robotics, artificial intelligence, and industrial optimization. Their primary research areas include point cloud registration, robot path planning, human–robot collaboration, and vision-language guided robotic manipulation. Li’s major contributions include developing SCANet, a spatial and channel attention-based network for partial-to-partial point cloud registration, which has garnered 21 citations and advanced 3D perception for robotics. In path planning, Li proposed the golden sine grey wolf optimizer (GSGWO), an improved metaheuristic algorithm that addresses slow convergence in obstacle-crossing robots, earning 18 citations since 2024. Li has also tackled practical challenges in manufacturing, notably researching balancing problems in human–robot collaborative assembly lines for SMEs, considering ergonomic risk and cost—work that has already attracted 8 citations. Most recently, Li introduced a 6-DoF grasp detection method leveraging vision-language guidance, achieving 1 citation and demonstrating potential for more intuitive, interactive robotic grasping. Through these contributions, Li is establishing a reputation for developing computationally efficient, real-world applicable solutions that enhance robot autonomy and human-robot interaction.