DDP-WM: Disentangled Dynamics Prediction for Efficient World Models

Understand

World models are essential for autonomous robotic planning.

• However, the substantial computational overhead of existing dense Transformerbased models significantly hinders real-time deployment.
• To address this efficiency-performance bottleneck, we introduce DDP-WM, a novel world model centered on the principle of Disentangled Dynamics Prediction (DDP).
• We hypothesize that latent state evolution in observed scenes is heterogeneous and can be decomposed into sparse primary dynamics driven by physical interactions and secondary context-driven background updates.