AI 中文总结
SG-WAM是自引导的几何感知世界动作模型框架，通过策略衍生空间联合优化动态预测等，在LIBERO等数据集上实现高成功率，性能优于基线。
AI 中文摘要
世界动作模型（WAMs）将动作生成与未来状态预测相结合，其有效性取决于未来动态是否在既与动作生成对齐又足够几何感知的空间中建模，以捕捉动作改变场景的位置和方式。现有WAMs通常仅满足部分需求，要么依赖感知负荷重的观测空间目标，要么依赖未针对动作相关性和几何进行联合结构化的辅助潜在空间。我们提出SG-WAM，一种直接在策略衍生表示空间中学习几何感知动作条件动态的自引导框架。SG-WAM引入可学习动态标记和自引导世界预测器，其在干预机器人动作的条件下预测这些标记的未来潜在状态。预测目标由同一策略骨干的指数移动平均副本生成，在动作专家使用的表示族内提供稳定监督。几何监督进一步结构化策略图像-标记表示，为动态标记提供空间接地上下文，并产生既与动作相关又几何感知的未来对齐空间。潜在未来预测、几何接地和流匹配动作生成在统一框架中端到端联合优化。基于0.9B模型且无大规模具身预训练，SG-WAM在LIBERO上达到98.5%的平均成功率，在LIBERO-Plus上达到73%，在分布内和分布外真实世界评估中均优于强基线。
英文摘要
World Action Models (WAMs) couple action generation with prediction of future states. Their effectiveness depends on whether future dynamics are modeled in a space that is both aligned with action generation and sufficiently geometry-aware to capture where and how actions change the scene. Existing WAMs typically satisfy only part of this requirement, relying on either perceptually heavy observation-space targets or auxiliary latent spaces that are not jointly structured for action relevance and geometry. We propose SG-WAM, a self-guided framework that learns geometry-aware action-conditioned dynamics directly in the policy-derived representation space. SG-WAM introduces learnable dynamics tokens and a Self-Guided World Predictor that forecasts their future latent states conditioned on intervening robot actions. Prediction targets are generated by an exponential moving average copy of the same policy backbone, providing stable supervision within the representation family used by the action expert. Geometric supervision further structures the policy image-token representations, providing spatially grounded context for the dynamics tokens and yielding a future-alignment space that is both action-relevant and geometry-aware. Latent future prediction, geometric grounding, and flow-matching action generation are jointly optimized end-to-end in a unified framework. Built on a 0.9B model without large-scale embodied pretraining, SG-WAM achieves 98.5% average success on LIBERO and 73% on LIBERO-Plus, while outperforming strong baselines in both in-distribution and out-of-distribution real-world evaluations.
