ACID：基于逆动力学的动作一致性用于世界模型规划

摘要

决策时间规划结合动作条件世界模型已成为具身控制领域的主流范式。然而，标准规划代价仅根据候选轨迹的预测终端状态与目标的接近程度进行评判，忽略了中间过渡状态的可实现性——预测轨迹可能看似合理，但实际环境交互却会偏离该轨迹。本文提出ACID框架，这是一种引入循环动作一致性的决策时间规划方案：通过逆动力学模型从预测过渡状态反向推理出的动作，应能恢复原始条件动作。我们通过尺度不变自适应权重将这种逐步残差融入规划代价。在涵盖刚体与非刚体操作、关节控制及视觉导航的四个动作条件世界模型与六项任务中，ACID持续提升规划性能，并在显著降低规划计算量的前提下保持与基线相当的精度。

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.