arXiv (All)
open access
IMLE-VLA: Fast Single-Step Action Generation for Vision-Language-Action Policies
Vision-language-action (VLA) policies leverage pretrained vision-language backbones to achieve strong cross-task generalization. A leading design couples this backbone with a dedicated continuous action head trained via diffusion or flow matching. However, such heads rely on iterative multi-step sam
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
- CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies#927297
- VLA Foundry: A Unified Framework for Training Vision-Language-Action Models#124125
- Frequency-Conditioned Flow Matching for Vision-Language-Action Models#997290
- CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies#138967
- ICI-VLA: In-Context Imitation with Spatiotemporally Aligned Demonstrations for Vision-Language-Action Models#984493
Record · ID 998490
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