SG-WAM：幾何感知策略空間中的自引導世界建模

摘要

世界動作模型（WAMs）將動作生成與未來狀態預測耦合。其有效性取決於未來動態是否在一個既與動作生成對齊、又具備足夠幾何感知能力以捕捉動作在何處及如何改變場景的空間中建模。現有的WAM通常僅滿足此要求的一部分，依賴於感知負擔較重的觀測空間目標，或未被同時針對動作相關性與幾何結構進行設計的輔助潛在空間。我們提出SG-WAM，一個自引導框架，直接在策略推導的表徵空間中學習幾何感知的動作條件動態。SG-WAM引入可學習的動態標記與自引導世界預測器，該預測器基於機器人介入動作來預測其未來潛在狀態。預測目標由同一策略骨幹的指數移動平均副本生成，在動作專家所使用的表徵家族內提供穩定的監督。幾何監督進一步結構化策略的圖像標記表徵，為動態標記提供具空間基礎的上下文，並產生一個既具動作相關性又具幾何感知的未來對齊空間。潛在未來預測、幾何基礎與流匹配動作生成在統一框架中進行端到端聯合優化。SG-WAM基於0.9B參數模型，無需大規模具身預訓練，在LIBERO上達到98.5%的平均成功率，在LIBERO-Plus上達到73%，同時在分布內與分布外的真實世界評估中均優於強基線。

English

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.