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About this paper
Hao Wu, Yuqi Li, Yizhou Zhao, Kun Wang, Xiao Luo, Yu Cheng, Bo An, Yingli Tian and 2 more
RoboImagine post-trains a robot video-prediction model by generating diverse futures for the same observations, actions, and instruction, then combining pairwise semantic feedback with visual consistency feedback. In a controlled RT-1 comparison, it achieved 22.57 dB PSNR versus 21.31 dB for continued supervised fine-tuning. This improvement concerns offline prediction and required more training compute; it does not establish better robot control.
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