PHI: A Hierarchical World Action Model with Selective Prediction and Force Feedback for Humanoid Bimanual Manipulation

Humanoid bimanual dexterous manipulation requires coordinated dual-arm and finger actions under evolving contact interactions. Existing world action models (WAMs) offer a promising way to model such future dynamics, but primarily optimize future representations for prediction, leaving their utility for downstream action generation implicit. We introduce PHI (Predictive Hierarchical Intelligence), a hierarchical world action model for humanoid bimanual manipulation that integrates selective future prediction with force feedback. Rather than reconstructing or preserving the complete future observation, PHI selectively learns compact future visual representations that retain information useful for action generation. A hierarchical predictive pathway first extracts task-relevant future representations from privileged future observations during training and then learns to infer them from the current interaction context at deployment. To complement visual prediction in contact-rich interactions, PHI further incorporates a temporal force encoder that captures recent force evolution and provides physical feedback about contact establishment, loading, and interaction changes. The predicted future representation and force feedback are jointly integrated to guide action generation. Across five real-world humanoid bimanual manipulation tasks, PHI achieves an aggregate success rate of 82.0%, outperforming the strongest baseline by 23.3 percentage points and up to a 30.0 percentage points gain on unseen book instances. These results demonstrate PHI's effectiveness through selective future prediction and temporal force feedback.