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About

Yaoxian Song is an emerging robotics and artificial intelligence researcher whose work sits at the intersection of robotic manipulation, multimodal perception, and embodied AI. With a research trajectory spanning from foundational grasping algorithms to sophisticated knowledge-driven systems, Song has steadily advanced the capabilities of intelligent robots operating in real-world environments. Song's most recognized contribution, "Deep Robotic Grasping Prediction with Hierarchical RGB-D Fusion" (2022, 43 citations), demonstrates their expertise in combining visual modalities to improve grasp reliability and precision. This work complements earlier efforts in depth-only grasping networks and tactile-visual fusion, reflecting a sustained commitment to robust multimodal sensing for manipulation tasks. Their 2024 paper on scene-driven multimodal knowledge graph construction for embodied AI (38 citations) marks a significant conceptual leap, bridging scene understanding with structured knowledge representation to enable smarter autonomous agents. Song has also made notable strides in contact-rich assembly through curriculum learning with vision-force fusion (20 citations) and fine-grained 6-DoF grasp detection grounded in part affordance. Their indoor navigation research further demonstrates range across perception and decision-making domains. Collectively accumulating over 160 citations, Song's body of work represents a compelling and growing contribution to next-generation intelligent robotics.