DatasetFeatured
BC-Z
Google BC-Z multi-task teleop dataset for zero-shot task generalization research.
Dataset
QT-Opt grasping data
Large-scale real grasping experience from QT-Opt — foundational closed-loop grasp learning resource.
Why: Historical large-scale real grasp experience
Overview
QT-Opt grasping data is catalogued on Embodied AI Hub as a curated dataset for embodied AI builders.
Large-scale real grasping experience from QT-Opt — foundational closed-loop grasp learning resource.
Why it matters: Historical large-scale real grasp experience
Use the official repository and docs as the source of truth for install pins, licenses, and hardware requirements.
Who it is for
Researchers, students, and builders comparing open embodied stacks.
Key highlights
- Large-scale real grasping experience from QT-Opt — foundational closed-loop grasp learning resource.
- Type: dataset; categories: dataset
- Catalogued for coverage completeness
When to use
- You are mapping the open embodied AI landscape
- You need a starting URL and judgment note before deep-diving
When not to use
- You need production SLAs or certified industrial support only
- License or hardware constraints are incompatible with your deployment
Getting started
- 1Open the official URL and read README install pins.
- 2Skim license and citation requirements.
- 3Run the smallest official example before scaling experiments.
- 4Cross-link related hub resources (sim / data / policy) for a full stack.
Papers & reading
Caveats & pitfalls
- Hub cards are curated summaries — always verify upstream docs.
- Stars and dates drift; treat extra.stars as approximate.
Content reviewed 2026-07-28