arXiv CS
open access
BURGER: Benchmarking and Understanding Robotic Generalization via Embodied Drawing
How effectively can vision-language-action (VLA) models learn diverse language-conditioned behaviors as training data scales? Investigating this question requires large and varied robot datasets, whose collection on physical robots remains costly. We introduce BURGER, a language-conditioned robotic
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
- BURGER: Benchmarking and Understanding Robotic Generalization via Embodied Drawing#1492140
- Exploring the Limits of Combinatorial Generalization in Embodied Manipulation#1326675
- AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding#1399887
- CAC-VLA: Context-Gated Action Conditioning for Vision-Language-Action Models#1109096
- RoboSPA: Can VLA Models Go Beyond Simple Scenes and Short-Horizon Tasks?#972256
Record · ID 1483041
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