Yiming Hao
Papers
1
Total Citations
1
H-Index
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About
Yiming Hao is a rising researcher at the forefront of embodied AI and robotic manipulation, with a focus on bridging video generation and physical task execution. Their key contributions lie in task-oriented hand-object interaction, where they develop generative models that produce realistic video demonstrations for robotic imitation learning. Hao’s most notable work, “TASTE-Rob,” tackles critical limitations in existing datasets like Ego4D—specifically inconsistent viewpoints and misaligned task semantics—by introducing a novel framework that generates coherent, task-specific hand-object interaction videos. This approach enables robots to learn generalizable manipulation skills from synthetic demonstrations, reducing reliance on costly real-world data collection. Though early in their career, Hao’s work has already garnered attention for its potential to scale robotic learning through video generation, with the paper accumulating citations rapidly since its 2025 release. Their research sits at the intersection of computer vision, robotics, and generative AI, offering a promising pathway toward more adaptable and autonomous robotic systems. Hao’s contributions are particularly impactful for students and researchers exploring how generative models can serve as a bridge between human demonstration and robotic execution in complex manipulation tasks.
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Top Papers
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