Yichen Song

National University of Defense Technology, Zhejiang University

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Total Citations

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About

Yichen Song is a researcher at the forefront of embodied intelligence and robotic cognition, with a focus on integrating knowledge representation and predictive modeling into autonomous systems. Their work bridges artificial intelligence and robotics, particularly in developing “robot brains” through knowledge graph completion—a critical advancement for enabling machines to reason and act in complex environments. One of their most cited papers, “A Novel Encoder-Decoder Knowledge Graph Completion Model for Robot Brain” (2021, 11 citations), proposes a deep learning architecture that enhances how robots understand and navigate relational data, laying groundwork for more adaptive cyber-physical systems. Song also addresses practical challenges in human-robot interaction, as seen in “A Novel Pet Trajectory Prediction Method for Intelligent Plant Cultivation Robot” (2023, 6 citations), which tackles collision avoidance by forecasting moving object paths—an innovative step toward safer, more responsive agricultural robots. Though early in their career, Song’s work demonstrates a clear trajectory toward making robots not only smarter but also more context-aware, with applications spanning service robotics, smart agriculture, and autonomous navigation. Their research is gaining traction as a foundational contribution to the next generation of intelligent machines.