Real work, everywhere.
Our own rigs record at real work sites: a head-mounted stereo pair and a wrist view, synced.
We capture, index, annotate and deliver the highest-quality egocentric video.
We're building toward millions of hours of diverse, high-quality video data.
Our own rigs record at real work sites: a head-mounted stereo pair and a wrist view, synced.
Every clip passes QC and is indexed per hand, action and task before it joins the lake.
Clips arrive in the shape robot learning needs.
We run the end-to-end system that enables efficient training cycles.
Wearable rigs record video where the work happens, mounted at the head, wrist, chest, cap or neck. Also captured: raw IMU and camera calibration.
Each hand's actions become time-stamped segments, so the lake can be searched by action.
ExploreWe query the lake for the video you need, then annotate it against your taxonomy.
# retrieve.py · illustrative
moments = lake.retrieve(
venue="construction site",
scenes=["rebar area",
"formwork area"],
skill_level="hard",
where="both hands on the tool",
exclude=["hand idle", "walking"],
)
moments.annotate(
taxonomy="your_taxonomy.yaml")
# → rebar and formwork moments, labelled
Each hand gets a caption for every segment, written against your taxonomy frame by frame.
Explore
Every segment ships with its hand, label, start and end time and caption, ready for training.
See the delivery format# s8-segment.json · excerpt
{
"clip": "S8",
"track": "right_hand",
"label": "Grasp",
"primitive": "grasp",
"start": 427.079,
"end": 430.876,
"text": "The hand holds the component…"
}
10+ papers from our team, including X-LeBench and Ego-ST Bench.
Pulled from the Video Datalake. For partnerships, pricing, support or press, use the same form and our team will get back to you.
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