Yingbo Tang

Chinese Academy of Sciences, University of Chinese Academy of Sciences

Papers

Total Citations

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About

Yingbo Tang is a robotics researcher whose work lies at the intersection of computer vision, affordance reasoning, and task-oriented manipulation. Their key research areas include robotic grasping, object recognition, and in-context learning for autonomous systems. Tang’s major contribution, **AffordGrasp**, introduces a novel framework for open-vocabulary task-oriented grasping in cluttered environments. By inferring an object’s functional affordance—where and how to grasp it based on its intended use—Tang enables robots to perform manipulation tasks with greater contextual awareness and adaptability. This work has already garnered **4 citations** since its 2025 publication, signaling its early impact on the field. Additionally, Tang’s earlier research on **Key-Part Attention Retrieval** (2023, 2 citations) addresses the challenging problem of recognizing objects from different sub-classes within the same category, using few visual samples. This approach enhances robotic object recognition by focusing on discriminative key parts, improving performance on fine-grained classification tasks. Tang’s work is particularly notable for bridging the gap between perception and action, making robots more capable of understanding and interacting with their environment in human-like ways. Their research holds promise for advancing autonomous systems in manufacturing, service robotics, and assistive technologies.