Congcong Yin

Jiangnan University, Changzhou University

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Total Citations

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About

Congcong Yin is a researcher at the forefront of robotic manipulation, focusing on how machines perceive and interact with the physical world. Yin’s core research lies at the intersection of computer vision and robotics, specifically in object affordance detection—the ability of a robot to recognize what actions an object allows (e.g., graspable, pushable). Their major contributions include developing boundary-preserving networks that enhance the precision of affordance segmentation, enabling robots to identify functional regions of objects with sharp, accurate edges. This work, published in 2022, has already garnered 15 citations, reflecting its immediate impact on the field. Yin further advanced this area with a semantic edge-aware network (2021, 9 citations), which integrates high-level object semantics to improve detection robustness. Notably, Yin’s multi-modal framework (2023, 7 citations) bridges the gap between human demonstration and robot learning, allowing machines to acquire manipulation skills by observing and interpreting human actions through vision and language cues. This innovative approach promises to make robotic training more intuitive and efficient. With a growing citation footprint and a clear trajectory toward practical, human-centric robotics, Congcong Yin is shaping how robots understand and act upon their environment.