DSWAM: A Dual-System World Action Foundation Model for Fine-Grained Robot Manipulation

Jian Zhu; Jianjun Zhang; Taiyi Su; Tianbin Liu; Zhangyuan Wang; Kai Xie; Zitai Huang; Chong Ma; Youzhang He; Tianjian Wang; Hanyang Wang; Weihao Ding; Yi Xu

arXiv · 2607.04927

DSWAM: A Dual-System World Action Foundation Model for Fine-Grained Robot Manipulation

Jian Zhu·Jianjun Zhang·Taiyi Su·Tianbin Liu·Zhangyuan Wang·Kai Xie·Zitai Huang·Chong Ma·Youzhang He·Tianjian Wang·Hanyang Wang·Weihao Ding·Yi Xu

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Abstract

World Action Models (WAMs) provide a promising alternative to Vision-Language-Action (VLA) policies by using video-based world modeling as dense supervision for robot action learning. Existing WAMs excel at physically grounded execution, but typically lack the explicit language-level planning interface in VLM-based VLAs for decomposing coarse instructions. Such decomposition becomes important when household tasks involve complex multi-step goals, where coarse user commands need to be converted into sequences of fine-grained executable subtasks. Meanwhile, the field still lacks a fair real-robot comparison between VLA and WAM execution capabilities, since existing systems often differ in data, robot embodiments, and task protocols. To address both the decomposition gap and the need for a controlled WAM-VLA comparison, we introduce DSWAM, a Dual-System World Action Foundation Model for fine-grained robot manipulation. DSWAM keeps a System 1 WAM executor as the default control path and optionally activates a System 2 vision-language subtask planner only when task decomposition is useful. The planner predicts executable subtasks from short-term visual history and a global task prompt, while the WAM executor performs world-aware action generation for each instruction or subtask. The executor is trained with action prediction and video co-training, but inference directly predicts action chunks without explicit future video generation. To make this execution path practical on real robots, we further integrate TensorRT acceleration, asynchronous execution, and real-time chunking (RTC) so that policy queries do not block robot control. To provide a fair real-robot comparison with VLA policies, we build and evaluate DSWAM under the DeMaVLA real-world deformable manipulation setting with matched robot platform, pretraining data, post-training data, and evaluation criteria.

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BibTeX RIS

Jian Zhu, Jianjun Zhang, Taiyi Su, Tianbin Liu, Zhangyuan Wang, Kai Xie, Zitai Huang, Chong Ma, Youzhang He, Tianjian Wang, Hanyang Wang, Weihao Ding, Yi Xu. 2026-07-06. DSWAM: A Dual-System World Action Foundation Model for Fine-Grained Robot Manipulation. https://arxiv.org/abs/2607.04927

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