潜在世界モデルにおける決定メトリック整合性：MPC計画のための診断と行動条件付き目的関数
Decision-Metric Alignment in Latent World Models: Diagnostics and Action-Conditioned Objectives for MPC Planning
JEPAスタイルの潜在世界モデルで、ユークリッド距離をコストに使うMPC計画の性能が、潜在空間の距離が実際のタスク進捗と一致しない問題を指摘し、整合性を測る指標と改善手法を提案した。
著者: Jiawei Wang, Ke Rui, Yushen Zuo, Yichun Feng, Minglei Li
分類: cs.LG, cs.CV
原文アブストラクト
JEPA-style latent world models can use Euclidean distance to a goal latent as the cost for model-predictive control (MPC). Strong decoding of task variables, however, does not guarantee that this particular cost ranks candidate action sequences by real task progress. We call the latter property \emph{decision-metric alignment}. We introduce Plan-Real Spearman, which measures latent--real rank agreement on random plans, and CEM-stage Spearman, which measures the same agreement as cross-entropy-method (CEM) search concentrates its proposal. We analyze sufficient conditions under which latent distance preserves real-cost rankings, identifying encoder distortion, terminal rollout error, and candidate margins as the controlling quantities. Guided by the observed empirical alignment gap, DA-LeWM augments LeWM with inverse-dynamics and demonstration-conditioned goal-action heads. Across all our experiments, DA-LeWM accelerates convergence and achieves higher online success than LeWM, while probe scores remain similar. These results show that action-conditioned objectives improve the geometry used by Euclidean-cost, CEM-based latent MPC.