通过逆动力学约束动作一致性，提升世界模型规划的可靠性。
ACID: Action Consistency via Inverse Dynamics for Planning with World Models
- 用逆动力学反推动作，检查每步预测是否自洽
- 在6个任务上减少计算量，仍保持相同精度
- 适合需要高效可靠规划的机器人控制场景
基于动作条件的世界模型进行决策时规划已成为具身控制的主流范式。然而，标准规划代价仅根据预测终点状态与目标的接近程度评估候选轨迹，未验证中间过渡的可实现性——预测轨迹可能看似合理，但环境回放会偏离。本文提出ACID框架，引入循环动作一致性：通过逆动力学模型从预测转移反推的动作，应能恢复原始条件动作。将每步残差以尺度不变自适应权重融入规划代价。在四个动作条件世界模型和六个任务（涵盖刚体与柔体操作、关节控制、视觉导航）上，ACID始终提升规划性能，且以显著更少的规划计算量达到基线精度。
原文摘要 · Abstract (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.
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