arXiv (All)
open access
Uncertainty Quantification for Flow-Based Generalist Robot Policies
Generalist robot policies, such as vision-language-action models (VLAs) and world-action models (WAMs), combine powerful pretrained backbones with expressive generative action heads trained via flow matching on large-scale robotic datasets. Despite their strong empirical performance in robotic manip
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
- Bilinear Flow Policy: Distributional Extrapolation for Goal-Conditioned Visuomotor Imitation#1201778
- ProbeFlow: Training-Free Adaptive Flow Matching for Vision-Language-Action Models#1198881
- AdaVLA: Adaptive Step Flow Matching for Training-free Acceleration of Vision-Language-Action Models#999860
- Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments#238651
- Key-Gram: Extensible World Knowledge for Embodied Manipulation#200538
Record · ID 1187450
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