<ul data-eligibleForWebStory="false"><li>Data scaling has been successful in NLP and CV, but its effectiveness in robotic manipulation needs further exploration.</li><li>Task diversity is more critical than the quantity of demonstrations, aiding transfer learning to new scenarios.</li><li>Multi-embodiment pre-training data is not necessary for cross-embodiment transfer; models trained on single-embodiment data can efficiently transfer to different platforms.</li><li>Expert diversity, influenced by individual preferences and human demonstrations, can hinder policy learning; a debiasing method called GO-1-Pro addressed velocity ambiguity, resulting in significant performance gains.</li></ul>

Is Diversity All You Need for Scalable Robotic Manipulation?

Discover more