Packing and dispatch egocentric data pilot
A first-person data pilot covering wrapping, boxing, taping and sealing orders at a logistics site in West Bengal.
This page is a template. Everything marked [PLACEHOLDER] must be replaced with real project data before the page is indexed or shared.
- Client type
- [PLACEHOLDER: client type, e.g. robotics AI lab, region]
- Hours captured
- [PLACEHOLDER: hours captured]
- Task list
- [PLACEHOLDER: task list, e.g. wrapping, boxing, taping, sealing]
- Delivery format
- [PLACEHOLDER: delivery format, e.g. resolution, frame rate, file format, metadata schema]
Challenge
[PLACEHOLDER: What the client needed and why existing data was not enough, e.g. viewpoint, environment variety, consent trail.]
Packing and dispatch is a common target for Physical AI: the objects vary, the hand movements are fine-grained, and the tasks are repeated thousands of times a day in real warehouses.
Approach
[PLACEHOLDER: How the project was scoped: task categories, sites chosen, number of operators, schedule.]
We followed the standard pilot plan: site onboarding and operator recruitment in week 1, then capture, QC and delivery in week 2. See the 50–100 hour pilot for how this works.
Capture setup
[PLACEHOLDER: Rigs and mounts used, camera model, resolution, frame rate, additional sensors.]
Two sample clips from this kind of task, wrapping an order and sealing a carton, recorded head-mounted in 4K, are on the pilot page. Rig and metadata details are on the egocentric data collection page.
QC and audit
[PLACEHOLDER: Checks applied, share of clips that passed first time, issues found and fixed.]
Every clip carried operator ID, site, task and consent reference and passed an approval gate before delivery. Consent and site agreements are described on our compliance page.
Delivery
[PLACEHOLDER: How and when data was delivered, folder structure, total volume.]
What we'd do differently
[PLACEHOLDER: Honest lessons from the project and what changed in our process as a result.]