Active vision manipulationExpert
ActiveWAM: Evidence-Aware Active Vision for World-Action Models
ActiveWAM is a preprint describing a world-action model that jointly controls a dual-arm robot and a two-degree-of-freedom pan/tilt camera, learning when to keep a useful view and when to move to gather new evidence. It reaches 80.3% clean and 53.3% compound-shift success on the 50-task RoboTwin-AV benchmark, versus 76.3% and 33.3% for Fast-WAM, and completes 50.0% of two-stage physical kitchen tasks versus 23.3% for Fast-WAM. This matters because it removes test-time inversion and viewpoint annotations while improving active-vision manipulation under distribution shift.