Egocentric Shoe Repair Video Data

Egocentric shoe repair data is real-world video recorded from the point of view of a person who is repairing a shoe. The camera sits on the head, so the footage shows what the person sees: the hands, the piece being worked on and the surface under it. Alongside the video, each episode carries depth, 3D hand pose, contact events and frame-accurate action segments, all aligned on one hardware clock.

Teams search for it under several names, including shoe repair robot training data, a cobbler task video dataset and egocentric footwear repair data. They all describe first-person demonstrations of repair work, collected to a written spec. This page covers only what the Firsthand site states about it and marks everything else as Not stated.

The site states one shoe task in its commercial task catalog. The Shoe repair row reads "Remove worn soles or heels, cut and glue replacements and stitch and finish edges". That wording names five operations: removing worn soles or heels, cutting replacements, gluing replacements, stitching, and finishing edges.

Anything outside that wording is Not stated. That includes polishing or conditioning shoes, dyeing, stretching, replacing laces or zips, and cleaning, so none is claimed here. If your policy needs one of them, you write it into the spec and we scope it, because the catalog row is a starting vocabulary, not a fixed menu.

The row is rated Hard and listed as Available on request. It sits in the Repair Services sector, in an environment called Repair Shop, which the site words as covering appliance, bicycle, shoe, watch and furniture work. It is recorded with a head-mounted phone or a multiview rig.

Available on request means the work is scoped and recruited to spec when a buyer asks for it. The row is not listed as Active collection, so the site does not say that a shoe repair capture stream is running today. A first batch is therefore quoted against your spec rather than drawn from a stream that already exists.

The Repair Shop environment lists a shoe repair shop among its examples, next to electronics repair, an appliance workshop, a bicycle shop, watch repair and furniture restoration. It holds six rows in total. Five of them, including Shoe repair, are rated Hard, and the bicycle row is rated Medium.

The site defines two capture modes. A head-mounted phone records the first-person view of real work on site. A multiview rig with handheld grippers records a staged working set. The Shoe repair row lists both modes and does not say which applies, so the site does not settle whether a given batch is a real workplace or a staged set. That is agreed in the spec.

The site names no shop, venue or company, and does not state the number of sites, the regions or the venue type. It does not state capture at a customer own facility either. Environment, access and schedule are agreed in the written spec.

A head-mounted phone records the first-person view of the job, and a multiview rig adds synchronized third-person angles when the spec asks for them. On the staged set, the operator performs the task with handheld grippers, so the demonstration maps onto a robot end effector. A wrist-mounted camera is available alongside the head camera for close-range detail. Camera count, resolution, frame rate and bench layout are Not stated, so raise any of them in the spec.

Each episode follows the Firsthand episode schema: ten streams synced under 2 ms, with head and wrist RGB, depth, 3D hand pose with per-joint visibility, contact and grasp events, and frame-accurate action segments. For shoe repair, hand pose tracks the fingers as they hold and guide a piece, contact events mark when a hand meets or releases an object, and action segments mark each step of the job.

Object labels use the terms your spec sets. Repair counts, shoe types, tool and machine types, materials and adhesives are Not stated. Task durations, cycle times, throughput and the number of episodes are Not stated too. Success or quality labels, such as whether a repair was finished correctly, are Not stated, as are certifications and robot results. The site makes no safety-compliance claim for this work.

The catalog rates this row Hard, and it is one of five Hard rows in its environment. That rating is the site only statement on difficulty. The site gives no technical analysis of shoe repair and this page makes no claim about robot results.

What the row wording implies is a long sequence of different operations. Removing a worn part, cutting a replacement, gluing it, stitching and finishing an edge each use the hands differently, and the job passes through all of them in order on one piece. The viewpoint matters too. The site states that a robot or humanoid perceives from a head- or wrist-mounted camera, not a static overhead shot, and that egocentric capture matches that geometry.

A good spec treats each named operation as its own task. Removing a worn part, cutting a replacement, gluing it, stitching and finishing an edge each get a start state, an end state and a set of accept criteria. That keeps action segments consistent across contributors and lets you ask for more of one operation without re-collecting the others.

Delivery formats are RLDS, LeRobot and WebDataset, plus HDF5, zarr and Rerun .rrd. Shoe repair episodes use the same schema as every other Firsthand collection, so a loader that reads another Firsthand dataset reads this one without changes. The format table below sets each format against what its official documentation states.

Every batch is validated against signed accept criteria before it ships, with a reject log, and delivery is rolling, per validated batch. Rejected items are recaptured at our cost, and billing is per validated hour.

Not in standard capture. Force or tactile readings are Not stated for the Shoe repair row. The site separately offers a tactile glove tier as a pilot-ready option, in which glove signals are time-synced to a head-mounted stereo camera by hardware timestamps and an IMU. The site does not state that the tier has been used on shoe repair, so if your policy needs a contact signal, ask for it in the spec and agree the fit with us first.

Every episode links to a signed contributor release through a documented consent chain, and the consent record is part of the delivery. A vision model runs on every frame at ingest, flagging faces, heads, licence plates and any legible screen or document. Those regions are irreversibly blurred, and a human reviewer confirms the result before footage ships.

If the reviewer is unsure a region is fully covered, the episode is held or discarded, never downgraded and shipped. A repair shop can put colleagues and customers in frame, and can show a screen, which is why that pass matters here. The site does not state that shoe labels or brand marks are blurred, so raise any such need before the spec is signed. It states no in-home capture for this work.

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 up front. The site does not state how deep coverage is for the Shoe repair row, and it is Available on request, so plan on a quote rather than assuming the 48 hour figure.

The site also states program milestones. A pilot reaches its first delivery in two to three weeks, and a standing program reaches full rate in four to six weeks. Those assume capture coverage is already in place. On-demand top-ups run against the same spec and accept criteria after each training run. A batch size and a later-batch turnaround are Not stated, so both are quoted against your spec.

You describe the repair operations, the environment and the sequence your policy needs. We turn that into a written spec covering tasks, environments, capture tier, annotation depth and accept criteria, then quote reach, timeline and price before any capture begins. This is the custom egocentric data collection route that every Firsthand task family follows.

A Spec Pilot, a small and fast first batch, can prove the spec and accept criteria before you scale. Every collection ships under a perpetual, buyer-owned commercial license, with a reject log and signed accept criteria on every batch.