ActiveWAM: Evidence-Aware Active Vision for World-Action Models
ActiveWAM is a unified model that learns observation and manipulation together by treating active vision as an evidence-aware retain–acquire problem. It uses training-time inversion to preserve task evidence while learning executable camera motion and end-effector actions simultaneously, leading to significant performance gains in complex bimanual tasks.