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Graviti

GRTraining data for Physical AI & LLMs

Real-world data for machines that learn.

Graviti captures, annotates and licenses production-grade datasets for humanoids, robots and large language models — with consent and provenance built into every record.

Active capture locations

  • Egocentric video
  • 3D hand pose
  • Teleoperation
  • Multi-view RGB-D
  • IMU
  • Language narration
  • Expert SFT
  • Preference pairs
  • Agent trajectories

01Flagship dataset

Egocentric Household Activities. First-person video of real people doing real chores in real homes.

MUG .97REC 00:03:12.40CAM 01 · 1080P30 · IMU 200HZPICK_UP · MUG → POUR · WATER
GR-EGO-01Available

Head-mounted video of everyday household tasks — cooking, cleaning, laundry, tidying — captured by consented contributors in their own homes across dozens of countries. Every clip ships with dense action segments, 3D hand pose and natural-language narration, ready for training vision-language-action models and humanoid policies.

Hours of video
10,000+
Contributors
2,400+
Countries
38
Task taxonomy
600+ tasks

02Global capture network

A global effort to record how the world actually works.

We don't stage data in labs. Contributors and partner businesses capture real tasks in real environments across six continents, so your models see the diversity they will meet in deployment.

38Countries
2,400+Contributors
10,000+Hours captured
1,100+Expert authors

03Catalog

Two catalogs. One standard of quality.

Off-the-shelf datasets you can license today, each with a public spec and a downloadable sample.

04How we build data

From the real world to your training run.

One accountable pipeline, from capture hardware to delivered shards. The same pipeline powers our catalog and custom programs.

  1. 01

    Capture

    A vetted global network of contributors, operators and domain experts records data on calibrated devices — in homes, workplaces and labs.

    Head-mounted cameras · teleop rigs · expert authors

  2. 02

    Annotate

    Trained annotators and model-assisted tooling add hand pose, action segments, language, preferences and rationales.

    Hand pose · actions · narration · rubrics

  3. 03

    Verify

    Multi-pass human review, automated checks and contamination screens. Every batch ships with measured quality metrics.

    Two-pass QA · agreement metrics · audits

  4. 04

    Deliver

    Clean rights and provenance, delivered in the formats your training stack already reads — to your bucket, on your schedule.

    LeRobot · RLDS · WebDataset · Parquet · JSONL

05Delivery

Ready for your stack on day one.

Standard formats, documented schemas and data cards. Delivered to S3, GCS, Azure or Hugging Face — no conversion scripts required.

  • LeRobot
  • RLDS
  • WebDataset
  • Parquet
  • JSONL
  • MP4
load_sample.py
from lerobot.datasets import LeRobotDataset

# Point at the extracted Graviti sample
ds = LeRobotDataset("graviti/teleop-manipulation-sample", root="./sample")

frame = ds[0]
frame["observation.images.head"].shape   # (3, 480, 640)
frame["action"].shape                     # (14,)
ds.meta.tasks[0]                         # 'Place the red cup on the top shelf'

06Rights & compliance

Data you can actually ship a model on.

Clean rights are not an afterthought. Read how we license data on our company page.

  • Consent by design

    Every contributor signs a written, revocable agreement that explicitly covers AI training.

  • Provenance per record

    Each clip, episode or example traces back to its capture session, device and consent record.

  • Privacy protection

    Faces, screens and documents are blurred; PII is removed and audio excluded unless licensed.

  • Clear commercial license

    Perpetual rights to train and deploy models, with exclusive options for strategic partners.

Get started

Tell us what your model needs to learn.

Share your target tasks, modalities and volume. We reply within one business day with matching datasets, samples and a quote.