τ_0-VLA：一個以世界模型引導測試時計算的階層式機器人基礎模型

摘要

長期機器人操作要求機器人既能可靠地執行單一技能，也能在延伸任務中連貫地排列這些技能。大多數分層視覺-語言-動作（VLA）模型僅以單次前向傳遞做出每個此類決策，缺乏將額外計算分配給困難或重大選擇的機制。我們提出τ_0-VLA，一個分層機器人基礎模型，透過世界模型引導的測試時計算，將高階子任務生成形式化為可擴展計算的推論問題。在每個推論步驟中，高階策略使用執行記憶體生成子任務，並在必要時於提交輸出前搜尋替代方案。隨後，低階策略在多種機器人實體上執行所生成的子任務。該策略透過多模態共同訓練，在40,115小時的異質真實世界資料上進行訓練。在域內與分佈偏移的設定下，分配額外的測試時計算能大幅提升下一子任務的預測準確度，而這些提升轉化為長期機器人操作任務上更高的閉環成功率。

English

Long-horizon robot manipulation requires a robot to both execute individual skills reliably and sequence them coherently over extended tasks. Most hierarchical vision-language-action (VLA) models make each such decision with a single forward pass, leaving no mechanism to allocate additional computation to difficult or consequential choices. We introduce τ_0-VLA, a hierarchical robot foundation model that formulates high-level subtask generation as a compute-scalable inference problem through world-model-guided test-time computation. At each inference step, the high-level policy uses execution memory to generate a subtask and, when needed, searches over alternatives before committing to its output. A low-level policy then executes the generated subtask across multiple robot embodiments. The policy is trained on 40,115 hours of heterogeneous real-world data with multimodal co-training. Across in-domain and distribution-shifted settings, allocating additional test-time computation substantially improves next-subtask prediction accuracy, and these gains translate into higher closed-loop success on long-horizon robot manipulation tasks.