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About

Liulong Ma is a robotics researcher whose work focuses on enabling intelligent autonomous navigation for mobile robots. His key research areas include mapless robot navigation, deep reinforcement learning (DRL), and simultaneous localization and mapping (SLAM) in dynamic environments. Ma’s major contributions center on developing learning-based approaches that allow robots to navigate without pre-existing maps, moving from simple memorization to higher-level reasoning. His most cited work, "Learning to Navigate in Indoor Environments: from Memorizing to Reasoning" (2019, 15 citations), demonstrates how DRL can overcome the limitations of traditional map-dependent methods. He further advanced this field by exploring the use of RGB images as visual input for mapless navigation (9 citations), addressing the challenge of training reinforcement learning agents through extensive exploration. Beyond navigation, Ma contributed to robotics infrastructure with "Plantbot: A New ROS-based Robot Platform for Fast Building and Developing" (4 citations), and tackled the critical issue of visual SLAM in dynamic environments through semantic segmentation-based approaches (2 citations). His work bridges the gap between theoretical DRL methods and practical robotic applications, making him a notable contributor to the field of intelligent mobile robotics.