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2024-10-17 14:13:38

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Open Graph

title

description

Learning to Act from Actionless Videos through Dense Correspondences

image

site name

author

updated

2026-02-17 16:53:56

raw text

Learning to Act from Actionless Videos through Dense Correspondences Learning to Act from Actionless Videos through Dense Correspondences Po-Chen Ko Jiayuan Mao Yilun Du Shao-Hua Sun Joshua B. Tenenbaum Paper arXiv Code Framework Overview (a) Our model takes the RGBD observation of the current environmental state and a textual goal description as its input. (b) It first synthesizes a video of imagined execution of the task using a diffusion model. (c) Next, it estimates the optical flow between adjacent frames in the video. (d) Finally, it leverages the optical flow as dense correspondences between frames and the depth of the first frame to compute SE(3) transformations of the target object, and subsequently, robot arm commands. Real-World Franka Emika Panda Arm with Bridge Dataset We train our video generation model on the Bridge data ( Ebert et al., 2022 ), and perform evaluation on a real-world Franka Emika Panda tabletop manipulat...

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