What Is Physical AI Data Collection?
Physical AI refers to artificial intelligence systems that interact with the physical world — robots that manipulate objects, navigate spaces, and perform tasks alongside humans. Training these systems requires data that captures the physics, geometry, and variability of real environments.
Physical AI data collection is the process of producing this training signal at scale. Unlike data for language models or image classifiers, Physical AI training data cannot be scraped from the internet. It must be collected in the real world, with calibrated sensors, by people performing actual tasks.
The data must capture not just what happened, but how it happened: contact forces, spatial relationships, timing, and the causal structure of physical interactions.
Why Physical AI Cannot Train on Internet Data
Embodiment Mismatch
Internet video shows humans from third-person perspectives. Physical AI needs first-person egocentric data that matches the robot's sensor configuration. The observation space must align between training and deployment.
Missing Action Labels
Internet video has no joint positions, no force readings, no gripper states. Physical AI needs action-labeled data — the motor commands that produced the observed behavior — for policy learning.
No Physical Ground Truth
Images and video do not encode contact forces, material properties, or spatial depth with the precision robotics requires. Physical AI data must be multi-sensor and calibrated.
The Data Stack for Physical AI
Training embodied intelligence requires multiple synchronized data modalities captured in the real world. Each layer of the stack serves a distinct role in the learning pipeline.
Egocentric Video & Depth
First-person RGB-D from head or wrist cameras. The visual input stream that visuomotor policies consume at inference time. Synchronized color and depth at 30-60 fps provides the observation space for behavior cloning and transformer-based action models.
Action Trajectories
Joint positions, velocities, and torques from teleoperation. The output signal for behavior cloning and diffusion policy architectures. These trajectories encode the motor commands that produced the demonstrated behavior — the labels that supervised learning requires.
Force & Contact Data
Wrist force-torque, tactile arrays, and grasp force profiles. Essential for contact-rich manipulation — assembly, insertion, tool use. These signals capture what vision alone cannot: the physical interaction between end effector and environment.
Annotations & Metadata
Temporal task segmentation, action labels, object identities, environment descriptions. The structured layer that makes raw sensor data usable for supervised learning. Without annotations, sensor streams are just numbers — metadata gives them meaning.
How Humaid Powers Physical AI Pipelines
Humaid is a vertically integrated data collection platform built specifically for Physical AI. We handle the entire pipeline from task design to dataset delivery.
Task Protocol Design
We work with your robotics team to define the exact tasks, success criteria, and variability requirements. Protocols are designed for downstream model training — not just data collection — ensuring every demonstration is usable.
On-Site Collection with Calibrated Rigs
Trained operators collect data with multi-sensor capture rigs calibrated to your robot platform. We collect data where your robot will deploy — on manufacturing floors, in warehouses, in commercial kitchens — so the training distribution matches the deployment distribution.
Annotation & Quality Control
Every recording passes through temporal segmentation, action labeling, and multi-stage quality review. We deliver in your preferred format — HDF5, ROS bags, LeRobot, or custom schemas — ready for your training pipeline with no additional processing.
Inspect Physical AI Data in the Explorer
All Physical AI datasets collected by Humaid are accessible through the robotics data explorer. The explorer provides browser-based access to synchronized multimodal recordings — egocentric video, 3D body and hand pose, object detection, action segmentation, and raw sensor streams — with frame-level inspection controls.
For Physical AI teams, this means validation happens before training, not after. Review annotation accuracy, check sensor synchronization, verify action label boundaries, and download specific sequences — all without writing custom data loading code. Open the data explorer.
Get Physical AI Training Data
Tell us your target task, robot platform, and deployment environment. We will design a collection protocol, deploy operators, and deliver calibrated multimodal datasets ready for your training pipeline.