SG-WAM: Text-Grounded and Spatial-aware Semantic Guidance for World-Action Models
World-Action Models (WAMs) have emerged as a promising paradigm for robotic manipulation. However, most existing WAMs generate future videos and actions by relying mainly on visual cues rather than language instructions, since off-the-shelf text encoders embed instructions independently of visual observations. As a result, the videos predicted by these WAMs are often semantically misaligned with their corresponding language instructions, which degrades the accuracy of the predicted actions. To overcome this limitation, we propose SG-WAM, a semantic guidance method for world-action models that leverages a vision-language model (VLM) as a semantic planner to enhance the instruction-groundin...
| Authors | Junjie He, Junfeng Li, Zhide Zhong et al. |
|---|---|
| Primary category | cs.RO |
| Published | 9 August 2026 |
| Last updated at source | 9 August 2026 |
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