Decision-Metric Alignment in Latent World Models: Diagnostics and Action-Conditioned Objectives for MPC Planning

2026-08-19

AI Summary

研究解决了JEPA风格潜在世界模型中决策度量对齐问题，通过引入Plan-Real Spearman和CEM-stage Spearman方法，并提出DA-LeWM增强模型，提高了基于CEM的潜在MPC性能。

Abstract

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.

Problem

• decision-metric alignment
• latent world models
• model-predictive control
• Euclidean distance
• task progress

Innovation

• decision-metric alignment
• inverse-dynamics
• demonstration-conditioned