Dexterous Manipulation Training Data

Most manipulation data defaults to a wrist-level pose or a parallel-gripper state because it is simpler to log. But fine manipulation, tool use, in-hand reorientation, precision pinches happens at the fingertip, and that signal is lost the moment capture drops to wrist level.

Capturing it requires 3D hand pose tracked through the full reach-grasp-adjust cycle, with visibility flags for the fingers an object or the hand itself occludes, not a wide shot that loses finger detail.

A head-mounted camera keeps the hand and manipulated object in frame from the viewpoint a manipulation policy will actually see from, through the whole reach-grasp-release cycle. An optional wrist camera adds close-range detail for fine manipulation.

3D hand pose is tracked with visibility flags, and contact events mark exactly when and where the hand makes and breaks contact, all aligned to depth and video on one clock.

You describe the manipulation tasks your policy needs. We turn it into a written spec covering tasks, environments, objects, capture tier, annotation depth, and accept criteria, then quote reach, timeline, and price against that document before any capture begins.

The collection runs through the same seven-stage end-to-end custom collection pipeline as every Firsthand program: contributor recruiting, capture, per-batch QA, anonymization, and licensed delivery. Rejected items are recaptured at our cost.

Where coverage is already deep, the first validated batch lands in a median of 48 hours from signed spec. Thin environments typically take three to four weeks, because vetting operators there is the bottleneck, and we quote that timeline up front.

On-demand top-ups run against the same spec and accept criteria after each training run, so new dexterous manipulation data stays consistent with what you already hold.

Every custom collection ships under a perpetual, buyer-owned commercial license, and exclusivity is available so the data is never resold. Each episode links to a signed contributor release through a documented consent chain.

Faces, plates, and screens are detected and irreversibly blurred at ingest, and a reviewer confirms the result before anything ships. The anonymization record is delivered with the data.