Metadata-Version: 2.4
Name: embodiment-card
Version: 0.1.2
Summary: Validate and render robot embodiment metadata for dataset and VLA release review.
Author-email: AuraOne <opensource@auraone.ai>
License-Expression: MIT
Project-URL: Homepage, https://auraone.ai/open
Project-URL: Source, https://github.com/auraoneai/embodiment-card
Project-URL: Issues, https://github.com/auraoneai/embodiment-card/issues
Project-URL: Documentation, https://github.com/auraoneai/embodiment-card#readme
Project-URL: Specification, https://github.com/auraoneai/embodiment-card/blob/main/spec/embodiment-card-schema-v1.json
Project-URL: Changelog, https://github.com/auraoneai/embodiment-card/blob/main/CHANGELOG.md
Project-URL: Security, https://github.com/auraoneai/embodiment-card/security/policy
Keywords: dataset-cards,embodiment,huggingface,metadata,robotics,vla
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Documentation
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: license-file

# embodiment-card

`embodiment-card` validates and renders structured robot embodiment metadata for
dataset and vision-language-action release review.

## At a Glance

| | |
| --- | --- |
| Job | Make robot hardware, sensing, action, frame, control, environment, and limitation assumptions reviewable. |
| Built for | Robotics dataset curators, VLA researchers, release reviewers, and documentation owners. |
| Differentiator | Dependency-free schema validation plus Markdown and Hugging Face README rendering. |
| Produces | A validated JSON card or a human-readable embodiment card for release documentation. |

## Install

```bash
python -m pip install "embodiment-card==0.1.2"
```

## Verified Quickstart

Run from a source checkout:

```bash
embodiment-card validate examples/mock_so101_card.json

embodiment-card render examples/mock_so101_card.json \
  --hf-readme \
  --out /tmp/SO101-EMBODIMENT.md
```

The bundled SO-101 fixture validates and renders Hugging Face-compatible YAML
tags followed by an embodiment card.

## Required Disclosure

The v1 card requires:

- name and robot morphology;
- at least one sensor;
- action-space metadata;
- at least one coordinate frame;
- positive control rate;
- teleoperation method;
- environment assumptions;
- safety boundaries and known limitations.

Validation checks required fields and a small set of structural constraints. It
does not verify hardware, calibration, units, coordinate transforms, or the
truth of a disclosure.

## Runtime, Data, and Network Boundary

- The CLI reads one local JSON file and writes Markdown to a file or stdout.
- It does not inspect robot media, connect to hardware, contact Hugging Face, or
  make network requests.
- `--hf-readme` formats local text only; it does not create or update a Hub
  repository.
- No redaction is applied. Review cards for private paths, operators, sites, or
  hardware identifiers before sharing them.

## Limitations

- Validation covers required fields and simple structure only. It does not check
  calibration, coordinate transforms, units, or hardware truth.
- Rendered cards remain self-reported documentation, not a safety review,
  hardware certification, or deployment approval.

## Compatibility

`robostudio-engine` exposes a direct `robostudio card` integration that imports
`embodiment-card` when the package is installed or available in the AuraOne
monorepo. The standalone card format can also be embedded in any dataset or
model README.

## Publication Status

Verified on 2026-07-13:

- PyPI: [`embodiment-card==0.1.2`](https://pypi.org/project/embodiment-card/0.1.2/)
- GitHub release: [`v0.1.2`](https://github.com/auraoneai/embodiment-card/releases/tag/v0.1.2)
- Bundled cards describe mock tutorial datasets and do not establish hardware
  validation or safety assurance.

## Next Action

Fill a card from one real dataset's reviewed metadata, have the hardware owner
verify frames and control rate, then publish the rendered disclosure beside the
dataset.
