HERO: Learning Humanoid End-Effector Control for Visual Whole-Body Open-Vocabulary Object Grasping
This work develops a modular humanoid system for autonomous grasping of objects from natural language instructions. It combines large vision models for scene understanding with a learned end-effector tracking policy called HERO, which accurately controls the robot's arm. The system achieves strong generalization by separating object identification from precise physical control, allowing it to handle novel objects effectively.