AR-WAM: A Visual-Conditioned Agent-Ready World Action Model for Robotic Manipulation

Yicheng Jiang; Zesen Gan; Xiaobo Wang; Tianlun He; Chenxu Zhao; Minghui Wu; Xinyue Wang; Jiaxu Wang; Junhao He; Jianan Wang; Qiming Shao

arXiv · 2609.23578

AR-WAM: A Visual-Conditioned Agent-Ready World Action Model for Robotic Manipulation

Yicheng Jiang·Zesen Gan·Xiaobo Wang·Tianlun He·Chenxu Zhao·Minghui Wu·Xinyue Wang·Jiaxu Wang·Junhao He·Jianan Wang·Qiming Shao

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Abstract

As AI agents become increasingly capable, agent-driven robotic control is emerging as a compelling paradigm. However, prevailing vision-language-action (VLA) models and world action models (WAMs) still rely on natural-language instructions to specify manipulation tasks, an ill-suited interface for agent-driven control: referentially ambiguous, spatially imprecise, redundant with the agent's inherent language understanding, and entangling intent with execution. We present AR-WAM, a visual-conditioned, agent-ready world action model that replaces language with two complementary conditions: a visual grounding prompt (a bounding box of the target) denoting the interaction object and location, and a learnable operation token dictating the atomic skill to execute. Our compact 0.5B-parameter model, with a frozen pretrained visual encoder and no language encoder, predicts scene evolution within compact latent states while decoding actions, exposing the policy's intent through explicit, supervisable reasoning signals. A model-agnostic compatibility layer provides three primitives (detect, execute, and query) so that local VLMs or online agent APIs can drive the policy directly, with long-horizon memory and closed-loop error recovery delegated to the agent side. On RoboTwin 2.0, RMBench, and a real Astribot S1 dual-arm platform, AR-WAM matches the strongest baselines on standard manipulation (87.2% average success) and outperforms them on memory-dependent and real-robot long-horizon tasks, improving success rates by 5.9% and 36.7%, respectively, while maintaining the lowest inference latency (14.1 ms).

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BibTeX RIS

Yicheng Jiang, Zesen Gan, Xiaobo Wang, Tianlun He, Chenxu Zhao, Minghui Wu, Xinyue Wang, Jiaxu Wang, Junhao He, Jianan Wang, Qiming Shao. 2026-09-20. AR-WAM: A Visual-Conditioned Agent-Ready World Action Model for Robotic Manipulation. https://arxiv.org/abs/2609.23578

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