# google-deepmind/open_x_embodiment

Repository: https://github.com/google-deepmind/open_x_embodiment
Canonical: https://ross.abutalabs.com/products/open_x_embodiment
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Last push: 2025-11-05T23:45:27+00:00

## Health v2 (maintenance only)
Score: 50/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 50, release rhythm 35, longevity 74
- inputs: {"age_days": 1048, "days_push": 301, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2005, forks 123 (observed 2026-08-28T04:06:04.597260+00:00)

## What it is
Open X-Embodiment is a unified collection of open-sourced robot learning datasets from multiple institutions, formatted in the RLDS episode format for downstream training. It also provides RT-X model checkpoints (TensorFlow and JAX) trained on this data for robotic manipulation tasks.

## Use cases
- download large-scale robot manipulation datasets in a unified format
- train robot policies on multi-embodiment data
- run inference with pretrained RT-1-X checkpoints
- visualize robot episodes for research
- benchmark robotic learning models
- fine-tune vision-language-action models on robot data

## When to choose
- you need diverse, cross-embodiment robot data for training or evaluation
- you want pretrained RT-X checkpoints for robot manipulation research
- you work with RLDS/TensorFlow Datasets pipelines

## When to avoid
- you need real-time robot control beyond the supported observation/action spaces
- you need wrist or depth camera inputs, which the provided models do not use
- you want a general-purpose robotics simulator rather than datasets

## Facets
- artifact type: dataset
- maturity: active
- function: machine-learning, deep-learning, data-science
- domain: robotics, machine-learning, deep-learning
- platform: python, cloud
- tags: robotics-dataset, rlds, rt-x-models, imitation-learning, tensorflow-datasets, robot-manipulation, gpu

## Member repositories
- google-deepmind/open_x_embodiment (main) score 50

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:04.597260+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:01:34.813588+00:00, confidence not recorded.
  - readme: https://github.com/google-deepmind/open_x_embodiment (fetched 2026-08-28T04:06:04.597260+00:00, sha 52e34e6c1c67)
- Data as of 2026-08-30T08:39:29.467469+00:00.