机器人策略新框架LaWAM，不用生成视频就能预测场景变化，又快又准，成功率98.6%还低延迟，推荐做机器人控制的看看。
LaWAM通过潜在视觉子目标替代重建未来视频，在LIBERO基准上达到98.6%的成功率，在RoboTwin上达到91.22%，并在真实世界操作任务中取得竞争性表现。该模型每次动作块预测仅需187毫秒，延迟比像素空间世界行动模型低24倍。LaWAM的核心是潜在动作条件潜在世界模型（LaWM），利用预训练视觉基础模型的潜在空间预测未来观测特征。
LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies
Vision-Language-Action models (VLAs) leverage large-scale vision-language pretraining for semantic robot control, but often lack explicit foresight into how robot actions change the scene. World-Action Models (WAMs) address this limitation by conditioning policies on predicted futures, yet existing approaches typically rely on computationally expensive video generation with substantial pixel-level redundancy. We present LaWAM, a Latent World Action Model that exposes predictive dynamics to robot policies through compact latent visual subgoals instead of reconstructed future video. At the core of LaWAM is a latent-action-conditioned Latent World Model (LaWM). We obtain LaWM by training a latent action model in the latent space of a pretrained vision foundation model and repurposing its forward decoder to predict future observation features for scene evolution. LaWAM then conditions action generation on these predicted latent visual subgoals to enable dynamics-aware robot control. LaWAM achieves state-of-the-art or competitive success rates (SRs) across LIBERO (98.6% SR), RoboTwin (91.22% SR), and real-world manipulation tasks while retaining low-latency inference. LaWAM runs in 187 ms per action-chunk prediction and achieves up to 24x lower wall-clock latency than pixel-space WAMs.