Light-WAM: Efficient World Action Models with State-Fusion Action Decoding
World Action Models (WAMs) extend robot policy learning by incorporating future prediction as an additional training objective, encouraging the policy to encode task-relevant temporal structure in its representations. Current WAMs often rely on large-scale generative architectures that incur high training costs and inference latency, making them difficult to deploy as efficient closed-loop policies. We propose Light-WAM, a lightweight World Action Model for efficient robot manipulation. Specifically, it is built with a compact video backbone and performs future-video supervision in a downsampled latent space, reducing the cost of video co-training while retaining its benefits for represen...
| Authors | Ziang Li, Dongzhou Cheng, Yibin Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 June 2026 |
| Last updated at source | 6 June 2026 |
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