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About

Dr. Xiali Li is a robotics researcher whose work spans aquatic environmental protection, space exploration, and robotic manipulation. Her most impactful contributions focus on computer vision and deep learning for autonomous underwater robots, specifically targeting the critical problem of aquatic pollution. Her highly cited 2022 paper, "A modified YOLOv4 detection method for a vision-based underwater garbage cleaning robot" (48 citations), proposes a high-speed, high-precision garbage detection algorithm that enables an autonomous cleaning robot to identify and collect debris. This work is complemented by her 2021 study on "Pruning-Based YOLOv4 Algorithm for Underwater Garbage Detection" (15 citations), which optimizes the model for efficiency. Beyond underwater robotics, Dr. Li has made notable advances in space robotics, developing optimal impact and post-impact motion control strategies for flexible dual-arm space robots capturing spinning objects (2019, 10 citations). Her earlier work on underactuated robotic fingers—including a fully rotational joint mechanism (2016, 8 citations) and kinematic simulations (2017, 7 citations)—demonstrates her foundational expertise in mechanical design and grasping. Through these diverse contributions, Dr. Li is advancing autonomous systems that operate in challenging environments, from polluted waters to outer space.