Learning 4D Geometric Priors for Inference-Efficient World Action Models
World Action Models (WAMs) have shown strong potential for robotic manipulation by jointly modeling visual future dynamics and executable action sequences. However, existing video-action co-training methods primarily optimize appearance-oriented video latents, which may insufficiently capture the temporally evolving geometry required for precise manipulation. We propose MECo-WAM, a Multi-Expert Co-Training World Action Model that injects action-relevant 4D geometric priors into video-action representations while preserving the original lightweight inference graph. During training, MECo-WAM combines video and action experts with a lightweight 4D expert supervised by relational targets from...
| Authors | Jianjun Zhang, Jian Zhu, Taiyi Su et al. |
|---|---|
| Primary category | cs.RO |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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