Hi thanks for the great work! I was able to run lingbot-VA model locally and was able to get the same number as reported in paper. But I failed to reproduce the number for pi0.5 as reported in paper. Specifically, I run the robotwin data collection locally and regenerate the 2500 clean + 25000 random data and trained pi0.5 using the locally-collected mixed data. The config for pi0.5 follows #12 and I was using jax version of pi0.5. But the success rate I got is around 50-60%.
Easy: 56.86%; Hard: 54.14%.
Could you help me confirm the config for pi0.5? I am also wondering what is your data augmentation used for robotwin? Could the data augmentation contribute to the 20% SR gap? Thank you!
Hi thanks for the great work! I was able to run lingbot-VA model locally and was able to get the same number as reported in paper. But I failed to reproduce the number for pi0.5 as reported in paper. Specifically, I run the robotwin data collection locally and regenerate the 2500 clean + 25000 random data and trained pi0.5 using the locally-collected mixed data. The config for pi0.5 follows #12 and I was using jax version of pi0.5. But the success rate I got is around 50-60%.
Easy: 56.86%; Hard: 54.14%.
Could you help me confirm the config for pi0.5? I am also wondering what is your data augmentation used for robotwin? Could the data augmentation contribute to the 20% SR gap? Thank you!