Jiayuan Tong
Papers
3
Total Citations
48
H-Index
3
About
Jiayuan Tong is a robotics researcher specializing in intelligent manipulation and visual reasoning for autonomous systems. Their core research focuses on enabling robots to understand and interact with complex, cluttered environments—particularly object-stacking scenes—by integrating graph-based reasoning with deep reinforcement learning. Tong’s major contributions include pioneering graph-based visual manipulation relationship reasoning networks, which allow robots to infer hierarchical object relationships and determine optimal grasping sequences for stable, orderly manipulation. Their most-cited work, "Graph-Based Visual Manipulation Relationship Reasoning Network for Robotic Grasping" (2021), has garnered 23 citations and addresses the challenge of grasping target objects in stacked arrangements by modeling inter-object dependencies. A follow-up study on grasping fully occluded objects (2022, 21 citations) further extends these techniques to handle severe visual obstructions, demonstrating robust performance in real-world scenarios. Tong’s research bridges computer vision and robotics, advancing the field toward more intelligent, context-aware robotic interaction. Their graph-based approaches have been recognized for enabling advanced robot-environment interaction, with applications in manufacturing, logistics, and service robotics.
Research Focus
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Top Papers
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