MoMo: Conditioned Contrastive Representation Learning for Preference-Modulated Planning

Temporally contrastive representation learning induces a latent structure that reduces long-horizon planning to inference in a low-dimensional linear system. However, existing contrastive planning work learns a single latent geometry that cannot distinguish among multiple valid behaviors, thereby preventing it from trading task efficiency against risk exposure for the same start-goal query. We introduce MoMo, a preference-conditioned contrastive planner allowing a scalar user preference to continuously modulate plan conservativeness at inference time, without retraining. MoMo learns a joint conditioning of the representation geometry and latent prediction operator via Feature-Wise Linear Modulation and low-rank neural modulation, respectively. We show that our formulation preserves the probability density ratio encoded in the representation space, which is required for inference-driven contrastive planning, while retaining its inference-time efficiency. Across six environments, MoMo smoothly adapts plan safety to user preferences, yielding improved temporal and preferential consistency compared to state-augmentation baselines.