$\tau_0$-WM：一种用于机器人操作的统一视频-动作世界模型
$τ_0$-WM: A Unified Video-Action World Model for Robotic Manipulation
AI总结：
提出$\tau_0$-WM，一个统一视频-动作世界模型，通过共享视频扩散骨干集成策略学习、视频预测和动作评估，在长时域和精细操作任务上优于基线。
摘要：
机器人操作需要能够生成可执行动作并在物理执行前预测和评估其未来后果的模型。我们提出$\tau_0$-世界模型（$\tau_0$-WM），一个统一的视频-动作世界模型，在单个未来预测框架内整合了策略学习、视频预测和动作评估。基于共享的视频扩散骨干，$\tau_0$-WM提供两个互补接口。首先，一个视频动作模型从多视角观察、语言指令和机器人状态中联合预测未来视觉潜变量和连续动作块。其次，一个动作条件视频模拟器将候选动作块展开为多视角未来并预测密集的任务进度分数。该模型在大约27,300小时的实机遥操作、UMI风格交互、自我中心人类视频以及使用模态特定监督掩码的展开或失败轨迹上进行训练。在推理时，$\tau_0$-WM利用测试时计算来采样动作候选，通过重新去噪一致性对其进行排序，并对低质量候选调用基于模拟器的修正。在具有挑战性的长时域和精细机器人操作任务上，$\tau_0$-WM表现出优于其他相关基线的性能。
英文摘要：
Robotic manipulation requires models that generate executable actions while anticipating and evaluating their future consequences before physical execution. We present $τ_0$-World Model ($τ_0$-WM), a unified video-action world model that integrates policy learning, video prediction, and action evaluation within a single future-predictive framework. Built on a shared video diffusion backbone, $τ_0$-WM provides two complementary interfaces. First, a video action model jointly predicts future visual latents and continuous action chunks from multi-view observations, language instructions, and robot state. Second, an action-conditioned video simulator rolls out candidate action chunks into multi-view futures and predicts dense task-progress scores. The model is trained on approximately $27{,}300$ hours of real-robot teleoperation, UMI-style interaction, egocentric human videos, and rollout or failure trajectories using modality-specific supervision masks. At inference time, $τ_0$-WM uses test-time computation to sample action candidates, rank them with re-denoising consistency, and invoke simulator-based rectification for low-quality candidates. On challenging long-horizon and fine-grained robotic manipulation tasks, $τ_0$-WM shows superior performance over other relevant baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
