Egocentric Video Data Collection for a Robotics AI Program
A robotics AI company needed first-person video of humans performing everyday physical tasks, captured consistently across diverse environments and body types, to train an embodied AI model to generalize beyond a single lab setting.
Client snapshot
| Industry | Robotics and embodied AI |
|---|---|
| Region | Multi-country field collection across Africa, Southeast Asia and the Americas |
| Engagement type | Egocentric physical task video collection |
The challenge
The client's existing dataset was collected almost entirely in a single country and demographic, which limited how well its model generalized to real-world household and workplace environments elsewhere. It needed field-based collection at volume, with consistent camera and task-structure standards, across markedly different environments.
The approach
Jwuma recruited field-capable contributors across several countries, equipped and briefed them to a documented recording standard, and collected first-person video of the specified task set across a deliberately diverse range of homes, workplaces and body types. Every session included explicit consent covering likeness and location data, and task-performance accuracy was verified during review alongside video quality.
Results
The client received a geographically and demographically diverse video dataset collected to a consistent methodology, materially broadening the environments its model had trained examples from, within the program's original timeline.
Frequently asked questions
Why does physical AI training data need to be collected in multiple countries?
Because a model trained on a narrow range of environments and body types generalizes poorly to real-world conditions outside that range.
How is recording consistency maintained across many contributors and locations?
Through a documented camera and task-structure standard that every contributor is briefed against before collection begins.
What consent is required for this type of data?
Explicit consent covering likeness and location data, in addition to standard data-use consent, collected at the point of each recording session.
Related case studies
Facial and Identity Verification Data for a Global Mobility Platform
A global mobility and ride-hailing platform needed verified facial and identity data across multiple regions to strengthen a driver and rider safety verification system, under strict consent and biometric-handling requirements.
RLHF Evaluation for a Conversational AI Model
A conversational AI company needed a structured, scalable human evaluation pipeline to rate chatbot response quality for its RLHF fine-tuning process, after its existing ad hoc rater pool produced inconsistent scoring.
Multilingual Content Moderation and Product Data for an E-Commerce Marketplace
A cross-border e-commerce marketplace needed multilingual content moderation and product catalog annotation to keep listings compliant and searchable across the languages its sellers and buyers actually used.
Published 2026-10-02 by Jwuma, operated by Corpshore AI. This case study is an anonymized composite representative of the kind of work Jwuma performs in this industry, described by industry, region and engagement type rather than by company name, since this engagement is not yet cleared for public naming. Discuss a similar program at client.corpshore.ai.