arXiv (All)
open access
REFACTOR-VLA: Unsupervised Library Learning of Typed Motor Programs
Most vision-language-action (VLA) models -- OpenVLA, $π_0$, RT-2, RDT-1B -- are monolithic: they emit raw motor commands or short action chunks without organizing behavior into reusable abstractions, so they degrade on long-horizon tasks and resist interpretation. Existing skill-discovery methods si
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
- Visible Touch: Rendering Contact for Visuomotor Policies#1083622
- CompVLA: A Variable Compliance Vision-Language-Action Model for Contact-rich Manipulation#1049353
- Faster and Better? Benchmark Bugs and Design Limitations Distort the Evaluation of Vision-Language-Action Acceleration#1142080
- Guide, Think, Act: Interactive Embodied Reasoning in Vision-Language-Action Models#1156352
- Spike-driven Vision-Language-Action Model#1144481
Record · ID 821174
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