τ_0-WM: A Unified Video-Action World Model for Robotic Manipulation
Authors: name: Pengfei Zhou, name: Shengcong Chen, name: Di Chen, name: Jiaxu Wang, name: Rongjun Jin, name: Bingwen Zhu, name: Yike Pan, name: Songen Gu, name: Kuanning Wang, name: Shufeng Nan, name: Xingyu Qiu, name: Chenhao Qiu, name: Pu Yang, name: Yunuo Cai, name: Jianxiong Gao, name: Yifan Li, name: Yanwei Fu, name: Xiangyu Yue, name: Zhi Chen, name: Jianlan Luo

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.