arXiv (All)
open access
Can a Frontier VLM Serve as a Robot Policy for Memory-Dependent Manipulation?
We use a frozen frontier vision-language model, GPT-6-Astra, directly as the robot policy on RoboMME, a benchmark of 16 memory-dependent manipulation tasks spanning temporal, spatial, object, and procedural memory. The model keeps the whole episode in context and outputs every end-effector waypoint
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
- Mem-World: Memory-Augmented Action-Conditioned World Models for Persistent Robot Manipulation#1122473
- Veo-Act: Enhancing VLA Policies with Frontier Video Models#1012179
- EvoMem-VLA: State-Evolution Memory for Long-Horizon Robot Manipulation#1201431
- Forward Dynamics Models#1297469
- EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies#1131581
Record · ID 1342226
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