DatasetFeatured
Language-Table
Real-robot language-conditioned rearrangement dataset and sim from Google Robotics.
DatasetFeatured
TACO
Multimodal tabletop manipulation with tactile signals — useful for contact-rich learning.
Why: Tabletop set with tactile modalities
Overview
TACO is catalogued on Embodied AI Hub as a curated dataset for embodied AI builders.
Multimodal tabletop manipulation with tactile signals — useful for contact-rich learning.
Why it matters: Tabletop set with tactile modalities
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
- Multimodal tabletop manipulation with tactile signals — useful for contact-rich learning.
- Type: dataset; categories: dataset
- Marked recommended on the hub
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.
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