Papers
2
Total Citations
39
H-Index
2
About
Kai Mo is a rising researcher in robot manipulation, with a focus on deformable objects and sequential task planning. His work addresses some of the most challenging problems in robotics: enabling robots to perceive, plan, and execute complex manipulations of non-rigid materials like cloth. In his highly cited 2022 paper, "Foldsformer: Learning Sequential Multi-Step Cloth Manipulation With Space-Time Attention" (25 citations), Mo introduced a novel space-time attention mechanism that allows robots to learn chained actions for folding tasks, moving beyond simple goal-conditioned approaches. He further advanced the field with his 2024 work, "Learning Language-Conditioned Deformable Object Manipulation with Graph Dynamics" (14 citations), which leverages natural language instructions and graph-based dynamics models to enable multi-task learning and generalization to new tasks without requiring goal images. Mo's contributions are particularly notable for their practical impact on robotic laundry, garment handling, and other real-world applications. His innovative integration of attention mechanisms and language conditioning marks him as a key figure in the next generation of robotic manipulation research.
Research Focus
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Top Papers
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