Mingrun Jiang
Papers
2
Total Citations
34
H-Index
2
About
Mingrun Jiang is a rising researcher in robotics and embodied intelligence, whose work centers on enabling robots to generalize manipulation skills across unfamiliar objects and environments. His primary research area focuses on affordance learning—the ability for robots to understand and act upon the functional possibilities of objects—with a particular emphasis on semantic correspondence to bridge the gap between familiar and novel scenarios. Jiang’s most impactful contribution is the development of Robo-ABC (Affordance Beyond Categories), a framework that leverages semantic correspondence to allow robots to transfer interaction knowledge from known objects to completely unseen ones, moving beyond category-specific training. This work, published in 2024, has already garnered over 30 citations, signaling its immediate influence in the robotics community. By addressing the critical challenge of out-of-distribution generalization, Robo-ABC represents a significant step toward open-world embodied intelligence, where robots can adaptively interact with their surroundings. Jiang’s research holds promise for advancing autonomous systems in dynamic, real-world settings, from household assistance to industrial automation, and marks him as a key contributor to the next generation of adaptive robotic manipulation.
Research Focus
Key Achievements
Top Papers
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- 2