Liangliang Ren

Tsinghua University, Fuyang Normal University

Papers

Total Citations

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About

Liangliang Ren is a researcher whose work bridges the cutting edge of artificial intelligence and practical robotics, with a focus on spatial reasoning and adaptive mechanical design. His early contributions to computer vision are exemplified by his 2020 paper on "Spatial Geometric Reasoning for Room Layout Estimation via Deep Reinforcement Learning" (11 citations), which introduced a novel approach to understanding indoor environments by combining geometric reasoning with reinforcement learning—a method that enhances how machines perceive and navigate complex spaces. More recently, Ren has turned his attention to the challenging domain of mining robotics. His 2025 paper, "Design and motion analysis of a coal mine robot with variable wheel diameter" (7 citations), proposes an innovative robot design featuring a variable-diameter wheel mechanism based on gears, connecting rods, and sliding rails. This design allows the robot to adapt its wheel size to the unstructured, hazardous terrain of coal mines, significantly improving mobility and stability. Ren’s work demonstrates a unique ability to apply advanced computational techniques to real-world engineering problems, making him a notable figure in both AI-driven spatial analysis and robotic system design.