[Submitted on 13 Sep 2026]
Title:World-Action Models for Robot Learning and Control: A Survey
View a PDF of the paper titled World-Action Models for Robot Learning and Control: A Survey, by Zuxing Lu and 15 other authors
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Abstract:Robots operating in open environments act under partial observability, physical constraints, and dynamic task contexts. Beyond mapping observations and language instructions to actions, they must anticipate how candidate actions may affect future states and task-relevant outcomes. Recent advances in world models, video generation, and Vision-Language-Action (VLA) policies have motivated the development of World-Action Models (WAMs), which couple future world prediction with executable action generation. This survey provides a robotics-oriented review of WAMs. We clarify their scope relative to conventional world models, model-based reinforcement learning, action-conditioned video generation, and reactive VLA policies, and organize existing methods through a unified taxonomy covering representations, transition modeling, action interfaces, architectures, training pipelines, data modalities, and scaling strategies. We further review applications of WAMs in manipulation, navigation, and autonomous driving, and we summarize the datasets, benchmarks, metrics, and protocols used to evaluate WAM systems. Finally, we discuss key challenges in action alignment, world-action factorization, spatial and multi-view consistency, long-horizon memory, neural simulation for closed-loop policy learning, and efficient inference. Taken together, this survey aims to provide a concise technical foundation for integrating predictive world modeling with action generation, toward more reliable embodied robot intelligence. Project page: this https URL.
Comments: 19 pages
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.16074 [cs.RO]
(or arXiv:2609.16074v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.16074
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hongjia Zhai [view email] [v1] Sun, 13 Sep 2026 16:35:56 UTC (3,757 KB)
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