ACID: Inverse Dynamics Action Consistency Improves World Model PlanningACID：逆动力学动作一致性优化世界模型规划
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Researchers Gawon Seo, Dongwon Kim, and Suha Kwak introduced ACID, a decision-time planning framework that addresses trajectory misalignment in action-conditioned world models by using inverse dynamics to verify predicted transitions match reality.
ACID enforces cycle action consistency—the action inferred backward from a predicted transition should match the original conditioned action—and integrates this constraint into planning costs via adaptive weighting.
Tested on four action-conditioned world models across six tasks spanning rigid and deformable manipulation, articulated control, and visual navigation, ACID improved planning performance while reducing computational requirements compared to baseline methods.