7 citations · 10 across the 5 of their papers we have counts for
5 papers
Human Universal Grasping
Kevin Yuanbo Wu, Tianxing Zhou, Isaac Tu +5
Humans can grasp objects effortlessly, whereas multi-fingered robots are far from this level of generality. We argue that the most natural source of robot grasping data is from hum…
FoundationMotion: Auto-Labeling and Reasoning about Spatial Movement in Videos
Yulu Gan, Ligeng Zhu, Dandan Shan +8
Motion understanding is fundamental to physical reasoning, enabling models to infer dynamics and predict future states. However, state-of-the-art models still struggle on recent mo…
Slot-Level Robotic Placement via Visual Imitation from Single Human Video
Dandan Shan, Kaichun Mo, Wei Yang +4
The majority of modern robot learning methods focus on learning a set of pre-defined tasks with limited or no generalization to new tasks. Extending the robot skillset to novel tas…
Reconstructing Hands in 3D with Transformers
Georgios Pavlakos, Dandan Shan, Ilija Radosavovic +3
We present an approach that can reconstruct hands in 3D from monocular input. Our approach for Hand Mesh Recovery, HaMeR, follows a fully transformer-based architecture and can ana…
Understanding Human Hands in Contact at Internet Scale
Dandan Shan, Jiaqi Geng, Michelle Shu +1
Hands are the central means by which humans manipulate their world and being able to reliably extract hand state information from Internet videos of humans engaged in their hands h…