Towards VLA-Dreamer: Refining VLA Behavior Using World Models
Vision-Language-Action models (VLAs), while showing strong potential for robot control, require massive amounts of high-quality imitation learning data. Moreover, the absence of an explicit world model casts further doubt on their control capabilities. In this concept paper, we propose a novel architecture that addresses sample efficiency in VLAs by training a predictive world model on the embedding space of the VLA's vision encoder. We hypothesize that these embeddings are action-relevant and usable for future prediction. To this end, we propose using the suggested architecture to investigate how well these embeddings predict the future based on actions, as the inability to do so would m...
| Authors | Parsa Mastouri Kashani, Jan-Gerrit Habekost, Stefan Wermter |
|---|---|
| Primary category | cs.RO |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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