ABot-M0.5：统一移動與操控的世界行動模型

摘要

移動操作是通用型機器人的關鍵能力，但對於當前的具身學習方法而言仍具挑戰性。VLA策略通常僅具反應性，缺乏明確的世界建模；現有的世界動作模型（World Action Models, WAMs）在與移動操作的結構對齊上仍有不足：它們在粗略的影片片段上運作，建模糾纏的導航-操作動作，並在不匹配自迴歸推論的監督下訓練反向動力學。因此，它們常遺漏細粒度的接觸動力學，遭受動作分佈衝突，並在長時域展開中累積誤差。我們提出ABot-M0.5，這是一個新的WAM，其基礎洞察為：移動操作需要在三個層級上進行對齊——時間粒度、動作空間以及訓練測試一致性。為了對齊時間粒度，我們引入中間潛在動作，用以捕捉局部視覺狀態轉換，並作為影片潛在變量與具身特定控制之間的橋樑動作空間。為了對齊動作空間，我們設計了一種雙層混合Transformer架構，不僅解耦了模態表示，還區分了異質的動作子空間（例如基座移動與手臂操作）。為了對齊推論條件，我們提出了夢境強制（dream-forcing）訓練策略，逐步在模型預測的影片上訓練反向動力學，從而改善自迴歸預測過程中的訓練測試對齊與魯棒性。在具挑戰性的移動與細粒度操作基準測試上進行的實驗表明，ABot-M0.5在長時域任務成功與細粒度控制準確度上均達到了最先進的性能。這些結果凸顯了粒度對齊、動作解耦與推論一致的世界動作建模的關鍵重要性。

English

Mobile manipulation is a key capability for general-purpose robots, yet remains challenging for current embodied learning methods. VLA policies are typically reactive and lack explicit world modeling, while existing World Action Models (WAMs) are still poorly aligned with the structure of mobile manipulation: they operate on coarse video chunks, model entangled navigation-manipulation actions, and train inverse dynamics under supervision that does not match autoregressive inference. As a result, they often miss fine-grained contact dynamics, suffer from action-distribution conflicts, and accumulate errors over long-horizon rollouts. We propose ABot-M0.5, a new WAM built on the insight that mobile manipulation requires alignment at three levels: temporal granularity, action space, and train-test consistency. To align temporal granularity, we introduce intermediate latent actions that capture local visual state transitions and serve as an bridging action space between video latents and embodiment-specific controls. To align action space, we design a dual-level Mixture-of-Transformers architecture that disentangles both modality representations and heterogeneous action subspaces such as base movement and arm manipulation. To align inference conditions, we propose the dream-forcing training strategy that progressively trains inverse dynamics on model-predicted videos, improving train-test alignment and robustness during autoregressive prediction. Experiments on challenging mobile and fine-grained manipulation benchmarks demonstrate that ABot-M0.5 achieves state-of-the-art performance in both long-horizon task success and finegrained control accuracy. These results highlight the critical importance of granularity-aligned, action-disentangled, and inference-consistent world-action modeling.