TCGI: Transition-Consistency-Guided Imagination for Visual Imitation Learning

World models support visual imitation learning by generating latent trajectories for policy optimization. Expert demonstrations provide transition information that can also guide the construction of these trajectories. We introduce Transition-Consistency-Guided Imagination (TCGI), which incorporates expert transition preferences into conservative model-based imitation learning (CMIL). A learned scorer distinguishes demonstrated latent transitions from unfiltered world-model rollouts. Its scores weight the fusion of candidate successor states, augment the imitation reward, and control when an ensemble-disagreement penalty is applied. These uses connect demonstration supervision to imagined state construction and policy optimization within a fixed imagination horizon. At deployment, the actor operates directly on inferred latent states. We evaluate TCGI on manipulation and locomotion tasks, with component ablations and parameter sensitivity analyses. Reported performance gains vary across tasks and metrics, with trade-offs between late-training scores and performance over recorded evaluation intervals. TCGI provides a concrete mechanism for incorporating expert transition preferences into latent imagination and a basis for examining how this guidance affects visual imitation learning.