arXiv (All)
open access
Causal Analysis of Robot Foundation Models Enables Efficiency Gains
Vision-language-action (VLA) models are a promising approach to general purpose robot manipulation. While the internal computation of vision-language models, which compose the backbone of VLAs, has been studied extensively, less is known about how their perceptual and semantic representations are tr
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
- HINT: Human-Intent Inception for Long-Horizon Robot Manipulation#973402
- A Survey on Efficient Vision-Language-Action Models#1129131
- Guide, Think, Act: Interactive Embodied Reasoning in Vision-Language-Action Models#1156352
- SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics#382879
- VLRLA: Deployment-Time Adaptation of Vision-Language-Action Models through In-Context Reinforcement Learning#1345340
Record · ID 1355739
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