arXiv (All)
open access
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 represe
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Control-Geometry Straightening for Sampling-Based Latent Planning#1108773
- SAGE: Subgoal-Conditioned Action Generation for Latent World Model Planning#1118656
- Metro-WM: Long-Horizon Latent Planning with Realisable Sub-Goals#1115824
- Accelerating Visual Policy Learning with Sampling-Based Model Predictive Control#1034388
- Accelerating Visual Policy Learning with Sampling-Based Model Predictive Control#978430
Record · ID 1130257
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive