τ_0-WM：用于机器人操控的统一视频-动作世界模型

Authors:Pengfei Zhou, Shengcong Chen, Di Chen, Jiaxu Wang, Rongjun Jin, Bingwen Zhu, Yike Pan, Songen Gu, Kuanning Wang, Shufeng Nan, Xingyu Qiu, Chenhao Qiu, Pu Yang, Yunuo Cai, Jianxiong Gao, Yifan Li, Yanwei Fu, Xiangyu Yue, Zhi Chen, Jianlan Luo

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Abstract:Robotic manipulation requires models that generate executable actions while anticipating and evaluating their future consequences before physical execution. We present $\tau_0$-World Model ($\tau_0$-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, $\tau_0$-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, $\tau_0$-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, $\tau_0$-WM shows superior performance over other relevant baselines.

Submission history

From: Shengcong Chen [view email]
[v1] Sun, 31 May 2026 05:35:36 UTC (2,646 KB)
[v2] Sun, 23 Aug 2026 13:19:46 UTC (2,646 KB)