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arXiv · 2509.00361

Generative Visual Foresight Meets Task-Agnostic Pose Estimation in Robotic Table-Top Manipulation

Chuye Zhang·Xiaoxiong Zhang·Wei Pan·Linfang Zheng·Wei Zhang
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Abstract

Robotic manipulation in unstructured environments requires systems that can generalize across diverse tasks while maintaining robust and reliable performance. We introduce {GVF-TAPE}, a closed-loop framework that combines generative visual foresight with task-agnostic pose estimation to enable scalable robotic manipulation. GVF-TAPE employs a generative video model to predict future RGB-D frames from a single side-view RGB image and a task description, offering visual plans that guide robot actions. A decoupled pose estimation model then extracts end-effector poses from the predicted frames, translating them into executable commands via low-level controllers. By iteratively integrating video foresight and pose estimation in a closed loop, GVF-TAPE achieves real-time, adaptive manipulation across a broad range of tasks. Extensive experiments in both simulation and real-world settings demonstrate that our approach reduces reliance on task-specific action data and generalizes effectively, providing a practical and scalable solution for intelligent robotic systems.

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Chuye Zhang, Xiaoxiong Zhang, Wei Pan, Linfang Zheng, Wei Zhang. 2025-08-30. Generative Visual Foresight Meets Task-Agnostic Pose Estimation in Robotic Table-Top Manipulation. https://arxiv.org/abs/2509.00361

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