Egocentric Data Collection for Embodied AI
We capture human physical movement, wrist-mounted camera footage, and 3D depth data using head-mounted devices from real people performing real tasks, then turn it into clean, robot-aligned training data that your model can use.
Why Egocentric Data Matters For AI
Embodied AI and Robotics
Train manipulation and locomotion policies on how humans actually grasp, lift, and move, not staged demonstrations.
Vision Language Action Models
Humanoid Foundation Models
Multimodal and Spatial AI
Combine RGB, depth, 6 DoF poses, and language in one synchronized dataset for richer context-aware learning.
Egocentric Data Use Cases
We capture egocentric demonstrations across the task categories that matter most to embodied AI and humanoid training.
Household
Teaching robots how humans tidy, clean, and organize the spaces they live in every day.
Kitchen
First-person footage of real cooking, prep, and cleaning tasks for manipulation models that need to work in human environments.
Industrial
Egocentric capture of assembly, tool handling, and workshop tasks for robots operating in structured work environments.
Agriculture
First-person capture of field inspection, crop handling, and equipment operation for AI systems built for outdoor and farm environments.
Logistics
First-person demonstrations of picking, packing, sorting, and palletizing to train warehouse and fulfillment automation.
Healthcare
Human activity data from care workflows and equipment handling for AI systems operating in clinical and assistive settings.
Retail
Egocentric footage of stocking, bagging, and shelf management to train robots working in customer-facing environments.
Hospitality
Egocentric footage of table setting, room reset, and detailed cleaning tasks for robots built to operate in hospitality and housekeeping environments.
Our Egocentric Data Collection and Curation Workflow
We define the task taxonomy, diversity quotas across environment and demographic, clip specifications, and required modalities before collection begins.
Trained operators capture synchronized RGB, pose, depth, and motion data using calibrated devices, with common timecode across every stream.
Every clip is automatically checked against hand-pose confidence, tracking drift, sensor sync, frame rate, resolution, and completeness thresholds. Clips that fail are flagged for reshoot, not silently dropped.
A stratified human audit reviews a sample across device, site, task, and collector for task correctness, occlusion, and safety, with rejected clips routed into a hard-negative pool rather than discarded.
Automated pre-labeling handles pose, depth, and segmentation, then human annotators add sub-action segmentation, contact-type labeling, and natural-language task descriptions to support the reasoning layer of your VLA model.
Every label set is checked against inter-annotator agreement thresholds and a calibrated gold-set, with low-agreement items escalated for review.
Final datasets are delivered training-ready, in formats like RLDS, HDF5, or WebDataset, aligned for robot embodiment and accompanied by a diversity-coverage report for every batch.
Our Egocentric Data Collection Services
Collecting Human-Driven Egocentric Data
AI-Ready Data Packages
We bundle human physical movement, wrist-mounted camera footage, and 3D depth data into clean, standardized files, including RLDS and HDF5 formats, so your models can ingest the data instantly.
On-Demand Trained Contributors
Get access to a managed workforce of human operators who remotely log task demonstrations. Every operator completes 8 to 40 hours of platform-specific training and certification before touching production data.
Automated and Manual Quality Assurance
Automated checks catch collections that fall short of technical specifications, like minimum hand visibility, and human reviewers verify every batch against our collection guidelines on top of that.
Bespoke Collection
We help companies gather private, proprietary datasets recorded on their own target hardware and workspace layouts, creating a unique asset competitors cannot copy.
Data Cleaning, Curation, and Labeling
Automated Editing
Using established pipelines like DROID and LeRobot, we ingest raw operator data and apply automated quality scoring, cutting manual data-prep labor by 40 to 60 percent.
Filtering Out Mistakes
Our curation tools filter out bad demonstrations before they reach your dataset. A handful of pristine examples consistently outperform a large volume of unverified data, and that is the standard we filter to.
VLA Annotation
Our deep labeling experience, including dense captioning, supports the reasoning and decision-making layer of your model, not just the perception layer.
Why Choose DDD?
Managed Global Workforce
A managed service built on a diverse, global workforce spanning multiple delivery centers, giving you the real-world human data physical AI needs, at the scale your training program requires.
End-to-End Solution
From operator recruitment and certification to data collection, curation, and annotation, we combine technology and trained talent into a single pipeline, so your team can stay focused on model development, not data logistics.
Certified Operators
Quality Assured
Rigorous operator training, proven collection protocols, and dual-stage quality assurance, automated checks paired with human review, ensure every batch meets the standard your model training depends on.
Security And Compliance
SOC 2 Type 2 certified, with validated controls for security, availability, and confidentiality
ISO 27001 certified information security management
HIPAA compliant for global privacy and healthcare data standards
GDPR compliant to handle your data with full European privacy standards.
TISAX Aligned for data security practices that meet automotive-grade information security standards.
Egocentric Data Collection for Training the Next Generation of Embodied AI
Frequently Asked Questions
It is the process of capturing first-person video, motion, and depth data from a human performing a task, using wrist-mounted, head-mounted, or smartphone-based devices, rather than a fixed third-person camera.
Third-person data observes a scene from a distance. Egocentric data preserves the exact spatial relationship between hands, tools, and objects as the person performing the task experiences it, which is what the signal manipulation models need most.
We use head and hand pose tracking hardware, wrist-mounted and head-mounted VR devices, depth-sensing smartphones, full-body motion trackers, and tactile or force-grasp gloves, deployed selectively based on your model’s modality requirements.
Yes. Our bespoke collection option records on your target hardware and your actual workspace layout, producing a dataset aligned to your specific embodiment rather than a generic benchmark.
We deliver in training-ready formats including RLDS, HDF5, and WebDataset, aligned for robot embodiment and ready for ingestion into common VLA training pipelines.
Consent and safety pre-checks happen during operator onboarding, and face-blur or PII protection is applied at the point of capture, not after the fact.
