Real-world data infrastructure

Building the Data Layer for Physical AI

Real-world human data that helps intelligent machines see, understand, learn, and act.

Real-world human activity converted into structured AI training data with bounding boxes, action labels and timestamps — Fig. 01: Human activity resolved into structured supervisionFrames / Boxes / Actions / Timestamps

01Capabilities

Data for machines that act in the real world

Egocentric Data

Human-centered video and demonstrations captured from real-world environments, first-person and continuous, providing high-signal training data for embodied AI.

Human Demonstration Data

Structured demonstrations of real-world tasks and workflows, performed by people at natural speed and annotated for robot learning.

Action & Object Data

Object interactions, action labels, timestamps and task-level annotations aligned frame by frame for physical AI training pipelines.

Robotics Data

Datasets shaped for humanoid robots, manipulators and embodied AI systems operating in unstructured, real-world spaces.

02Modalities

Training data across every capture modality

From single-camera footage to full VR teleoperation, we capture, structure and annotate real-world and robot data in the format your models actually train on: mono RGB, stereo, stereo+wrist, depth, teleoperation, VR/Quest/Pico, iPhone and OTS robot data.

Mono RGB Data

Single RGB camera

Single-camera RGB video for lightweight, high-volume data collection across everyday tasks and environments.

Stereo Data

Dual synchronized cameras

Calibrated stereo camera pairs for depth-aware scene understanding, giving models real 3D spatial context from two viewpoints.

Stereo + Wrist Data

Stereo head cams + wrist cam

Stereo head cameras combined with a wrist-mounted camera for close-range grasp and fine-manipulation detail. It's the standard rig for bimanual robot demonstrations.

RGB-D / Depth Data

Depth-aligned RGB

Depth-aligned RGB streams for precise 3D geometry, object pose estimation and scene reconstruction.

Teleoperation (Tele) Data

Direct robot teleoperation

Robot demonstrations recorded through direct human teleoperation, with joint and end-effector actions labeled frame by frame.

VR / Quest / Pico Data

Meta Quest & Pico headsets

Immersive teleoperation data captured through Meta Quest and Pico VR headsets, giving natural, embodied control for complex manipulation tasks.

iPhone Data

iPhone camera + LiDAR

Portable, high-fidelity capture using iPhone camera and LiDAR rigs for fast, in-the-wild data collection without custom hardware.

OTS (Off-the-Shelf) Robot Data

Commercial robot platforms

Demonstrations collected on commercially available, off-the-shelf robot arms and grippers, delivering production-ready data without bespoke rigs.

03Process

From the real world to AI-ready data

Capture

Real-world activity recorded at source with calibrated multi-sensor rigs.

Structure

Sessions segmented into clean, time-aligned streams and reviewed for integrity.

Annotate

Actions, objects, trajectories and timestamps labeled and reviewed in Foxglove, against a task taxonomy.

Foxglove

Deliver

Training-ready datasets delivered end to end as MCAP, JSON / JSON Schema, or your own schema, with full metadata and provenance intact.

MCAP · JSON · Metadata

04Sectors

Humanoid RoboticsWhole-body manipulation

Embodied AIPerception to action

Industrial RoboticsRepeatable task learning

Autonomous SystemsReal-world generalization

05Field

Real-world data collection

Multi-sensor capture in live environments: egocentric rigs, instrumented workspaces and robot-in-the-loop demonstrations.

Operator wearing a head-mounted camera rig recording egocentric data while assembling parts — Egocentric capture rigsHead-mounted / first-person

Multi-camera and depth-sensor capture setup recording a human demonstration in a real kitchen — Instrumented environmentsMulti-camera / depth

Robotic arm and human hands performing a pick-and-place task with trajectory overlays — Robot-in-the-loopTrajectory / grasp

Contact

Get the real-world data your models need.

Work With DataforAi daniel@dataforai.us