From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence
Artificial general intelligence ultimately requires agents that can reason and act in the physical world. Action models, vision-language-action policies, and world models have advanced this goal, while World Action Models (WAMs) are particularly promising because they connect candidate interventions with predicted consequences. However, progress remains fragmented: models use incompatible action spaces and prediction targets, datasets and tasks follow different conventions, and runtime systems expose limited interfaces for reuse and evaluation. We review the evolution toward WAMs and organize these limitations into three coupled gaps: model roles and representations, objectives and standa...
| Authors | Yuanzhi Liang, Xufeng Zhan, Haibin Huang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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