Ziluo Ding

HAF outperforms conventional VLA models in humanoid loco-manipulation by effectively managing complex motion coordination without the need for extensive computational resources.

Achieve human-like dexterity in humanoid robots by unifying visual-language cues with learned whole-body proprioceptive dynamics, outperforming prior methods in complex manipulation tasks.