SmolWAM: A Latent World-Action Model for Robotic Manipulation on Edge Devices
World-action models (WAMs) predict a robot's actions together with the observations those actions will produce. However, the strongest models rely on video diffusion transformers that are far too computationally expensive for resource-constrained edge devices. We present SmolWAM, a latent WAM that we derive from the 2B-parameter Cosmos Policy by keeping its pretrained trunk and rebuilding its inference interface: single-frame visual latents, cached text features that preserve the trunk's attention sinks, 12 of 28 transformer blocks, and 1,008 tokens from which actions and future visual latents are read out in one rectified-flow step. Trained in 3.6 GPU-hours, SmolWAM succeeds in 95.5% of ...
| Authors | Timi Owolabi, Hikmah Olawore, Frances Adelakun et al. |
|---|---|
| Primary category | cs.RO |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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