Control-Geometry Straightening for Sampling-Based Latent Planning
Joint-embedding predictive architectures enable planning with latent world models, but accurate transition prediction alone does not ensure that the planning objective is easy to optimize. We introduce Control-Geometry Straightening (CGS), a single auxiliary loss that learns planner-friendly representations by directly straightening control geometry for sampling-efficient planning. CGS matches pairwise cosine similarities among actions to those among corresponding latent differences only using local transitions from pixel-action pairs. The loss can be applied across world-model architectures using end-to-end learned or pretrained representations. Under linear-dynamics, our theoretical ana...
| Authors | Ziang Fu, Ning Ning |
|---|---|
| Primary category | cs.LG |
| Published | 28 September 2026 |
| Last updated at source | 7 October 2026 |
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