Delta-JEPA: Learning Action-Sensitive World Models via Latent Difference Decoding
Learning visual world models for planning requires compact latent dynamics that remain sensitive to actions, yet reconstruction-free joint-embedding objectives can collapse to action-insensitive representations. We propose Delta-JEPA, an end-to-end reconstruction-free world model that augments latent forward prediction with a Latent Difference Action Decoder (LDAD). Unlike inverse decoders that infer actions from concatenated endpoint embeddings, LDAD reconstructs the executed action from the latent displacement between consecutive observations. This displacement-level supervision directly regularizes transition geometry: adjacent embeddings cannot collapse without losing action informati...
| Authors | Zhenghao Zhang, Yuanxiang Wang, Zhenyu Guan et al. |
|---|---|
| Primary category | cs.AI |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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