arXiv (All)
open access
VLA-Pro: Cross-Task Procedural Memory Transfer for Vision-Language-Action Models
Vision-Language-Action (VLA) models have shown strong potential for general-purpose robotic manipulation, yet they still struggle to generalize to unseen tasks that necessitate transferring relevant experience across objects, scenes, and action patterns. This paper proposes VLA-Pro, a plug-and-play
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
- VLA-Retain: Benchmarking Cross-Dimension Effects of Vision Tuning in VLAs#1403494
- Adapting Vision-Language-Action Models to Unknown Visual Disruptions During Execution#1217681
- One-shot Human Video-Guided Adaptation for Vision-Language-Action Models#1354431
- Towards Zero-Shot Transfer Across Embodiments For Driving VLAs#924233
- Towards Zero-Shot Transfer Across Embodiments For Driving VLAs#920002
Record · ID 1400942
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