arXiv (All)
open access
Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agents
Language-conditioned manipulation requires both precise contact-rich control and robust reasoning over language, scenes, and long horizons. End-to-end Vision-Language-Action (VLA) models provide strong local visuomotor skills, but they are trained on in-distribution task trajectories and often fail
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
- Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agents#1040213
- Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agents#920382
- CompVLA: A Variable Compliance Vision-Language-Action Model for Contact-rich Manipulation#1049353
- DynamicVLA: A Vision-Language-Action Model for Dynamic Object Manipulation#1157258
- NS-VLA: Towards Neuro-Symbolic Vision-Language-Action Models#1069758
Record · ID 924614
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