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

2026-09-20

AI Summary

本文提出AR-WAM模型，通过视觉条件和可学习操作令牌替代自然语言指令，解决机器人操控任务中语言指令的局限性，提高任务成功率和推理效率。

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).

Problem

• vision-language-action models
• world action models
• natural-language instructions
• robotic manipulation
• agent-driven control

Innovation

• visual-conditioned
• agent-ready world action model
• visual grounding prompt
• learnable operation token
• model-agnostic compatibility layer