Siyi Tian

Papers

Total Citations

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About

Siyi Tian is a researcher specializing in intelligent robotics and autonomous navigation, with a focus on deep reinforcement learning and real-time perception systems. Their most cited work, "The application of path planning algorithm based on deep reinforcement learning for mobile robots" (2022, 3 citations), addresses a critical challenge in autonomous tour guide robots: efficient and reliable route planning in dynamic environments. Tian innovatively improves upon the traditional Deep Q-learning Network (DQN) by mitigating overfitting and overestimation, two common defects that hinder real-world deployment. This contribution enhances the adaptability and safety of mobile robots in crowded or complex tourist venues. In a complementary study, "A real-time localization algorithm based on feature point matching" (2022, 1 citation), Tian explores how video frame changes can be quantified to estimate camera movement, advancing low-cost, vision-based localization. By comparing state-of-the-art feature extraction methods, this work supports robust navigation without expensive sensors. Though early in their career, Tian’s research bridges the gap between theoretical reinforcement learning and practical robotics, laying groundwork for more intelligent, autonomous service robots. Their work is particularly relevant for students and engineers interested in applying deep learning to real-world robotic systems.