SCAR: Self-Supervised Continuous Action Representation Learning
Despite the central role of action in embodied intelligence, learning transferable action representations from visual transitions remains a fundamental challenge, particularly when world models must generalize across embodiments under limited data. We argue that action is not merely an auxiliary conditioning signal, but a distinct representational factor that decouples the controllable change from embodiment-specific actuation. In this work, we propose SCAR, a joint inverse-forward dynamics framework for learning unified action representations across embodiments from visual transitions. Built on a pretrained generative backbone, SCAR uses an inverse dynamics model (IDM) to infer latent ac...
| Authors | Hongjia Liu, Fan Feng, Minghao Fu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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