ACID：透過逆向動力學實現動作一致性以進行世界模型規劃

摘要

基於動作條件世界模型的決策時間規劃已成為具身控制的主流範式。然而，標準規劃成本僅根據預測的最終狀態與目標的接近程度來評估候選方案，卻未檢查中間轉移的可行性——這意味著預測軌跡看似合理，但環境實際展開卻可能偏離該軌跡。本文提出 ACID，這是一個引入循環動作一致性的決策時間規劃框架：由逆向動力學模型從預測轉移中反向推斷出的動作，應能恢復原本條件化的動作。我們透過一個尺度不變的自適應權重，將此每一步的殘差納入規劃成本中。跨越四種動作條件世界模型與六項任務（涵蓋剛性與可變形物體操作、關節控制及視覺導航），ACID 持續改善規劃效果，並在顯著減少規劃計算量的情況下達到與基準相當的準確度。

English

Decision-time planning with action-conditioned world models has become a popular paradigm for embodied control. However, the standard planning cost judges a candidate solely by how close its predicted terminal state lies to the goal, leaving the realizability of the intermediate transitions unchecked -- a predicted trajectory can look convincing while the environment rollout drifts away from it. In this paper, we propose ACID, a decision-time planning framework that introduces cycle action consistency: the action inferred backward from a predicted transition by an inverse dynamics model should recover the one that was conditioned on. We fold this per-step residual into the planning cost via a scale-invariant adaptive weight. Across four action-conditioned world models and six tasks spanning rigid and deformable manipulation, articulated control, and visual navigation, ACID consistently improves planning and matches the baseline's accuracy with substantially less planning compute.