Meng-Di Xu

Weave is presented, a unified framework for learning whole-body dexterous humanoid-object interaction from captured human demonstrations that converts captured human-object interactions into executable robot-object references through contact-aware retargeting and approach-motion completion.

R2S-Eval reveals that automated evaluation can outperform manual success counting by capturing nuanced differences in robot behavior quality.