Relational Geometry Forcing for World Action Models
World Action Models (WAMs) have shown strong potential for robot manipulation by jointly learning visual dynamics and executable actions.
ProofPaper ↗
Key points
- Recently, geometry-aware WAMs introduce depth or 4D prediction branches, whereas representation alignment methods supervise intermediate WAM features with pretrained 3D foundation models.
- Yet we find that such pointwise alignment benefits the video features but degrades action learning, since dense geometric feature targets overly restrict compact action features.
- To address this limitation, we propose Relational Geometry Forcing (RGF), which transfers geometric priors from a frozen 3D foundation model by aligning pairwise feature relations instead of pointwise features.
- Extensive experiments in simulation and the real world demonstrate the effectiveness of Relational Geometry Forcing, showing consistent gains across diverse manipulation scenarios and WAM paradigms.
Sources (1)
- [1]Relational Geometry Forcing for World Action ModelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 06:35 AM
World Action Models (WAMs) have shown strong potential for robot manipulation by jointly learning visual dynamics and executable actions.
Recently, geometry-aware WAMs introduce depth or 4D prediction branches, whereas representation alignment methods supervise intermediate WAM features with pretrained 3D foundation models.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 21, 2026How Representational Priors Shape Generalization in World Action Models
- Oct 21, 2026Agentic World Action Models
- Oct 20, 2026Plausible Future, Wrong Choice: Steering World-Action Models toward Intended Future
- Oct 20, 2026De-Diffusing WAMs: Action-Sufficient Direct Prediction for Robot Control
- Oct 20, 2026PLaCA: Learning Patch-Level Future Latent Change for Action Generation
- Oct 19, 2026PrismWAM: Decoupling State Representations in World Action Models