「 Ego Data for Physical AI 」
Ego Data for Physical AI
First-person task evidence for learning physical interaction, contact, failure, and recovery.
Ego data is synchronized first-person task data captured from the operator's point of action. It preserves what the person sees together with hand and tool motion, force, contact, task state, failure, recovery, and outcome.
「 What Ego Data Preserves 」
A recording becomes useful when the physical evidence shares one task clock.
Video alone shows appearance. Physical AI needs synchronized evidence that connects perception to action and outcome.
01
First-person and spatial video
Multi-view ego video preserves the operator's field of view, task object, workspace, and changing spatial context.
02
Hand, tool, and action motion
Pose and trajectories show how an operation unfolds, not only where the task ends.
03
Force and contact
Force, contact events, and state changes reveal when physical interaction begins and how it affects the environment.
04
Failure, recovery, and outcome
The episode preserves why execution diverged, what recovery followed, and whether the task ultimately succeeded.
「 Observation vs. Operation 」
Ego data is an input. Operational data is the compiled asset.
01
Observational ego data
A synchronized record of a task as it happened.
- first-person video
- motion and pose
- sensor and force streams
- task outcome
02
Operational data
A task-native asset that explains how the operation works.
- task stages and boundaries
- state and contact transitions
- failure causes and recovery
- quality and evaluation cases
「 From Capture to Asset 」
The Task Interface captures evidence. The Physical Data Compiler makes it usable.
ArcheBase aligns multimodal signals, reconstructs task structure, interprets physical causality, applies quality gates, and exports versioned assets for training and evaluation.
01
Perform
A person performs a real task with a multi-view wearable capture system.
02
Synchronize
Video, pose, motion, force, contact, and outcome align on one shared task clock.
03
Compile
Physical AI DBMS and the Physical Causal Model turn the episode into task-native operational data.
04
Deliver
Versioned datasets, failure cases, evaluation assets, and training-ready formats become reusable outputs.
「 What It Serves 」
One evidence base can serve multiple Physical AI jobs.
Pretraining and fine-tuning
Use diverse task stages, contacts, and outcomes to train policies and multimodal models.
Evaluation and regression
Build repeatable evaluation assets from success, failure, edge cases, and recovery.
Failure analysis
Retrieve episodes by task state, contact transition, failure cause, or recovery pattern.
Intelligence as a Service
Agents can call task retrieval, diagnosis, quality checks, and evaluation generation through Data APIs.
「 Technical Guide 」
What Is Ego Data for Physical AI?
Read the deeper technical guide to egocentric task data, synchronized physical evidence, and the path from human execution to robot learning assets.
01
What Is Ego Data for Physical AI?
The core guide to first-person task evidence and operational data.
02
Ego Data vs. Observational Robot Data
Separate viewpoint, evidence role, and compiled task meaning.
03
What a Human Task Episode Must Preserve
A field checklist for complete synchronized physical evidence.
04
Synchronizing Video, Motion, Force, Contact, and Outcome
Why trustworthy task semantics begin with time and calibration.
05
From First-Person Recordings to Operational Data
The compilation path from raw evidence to reusable assets.
06
Why Robot Training Needs Failure and Recovery Episodes
How deviations and corrections improve training and evaluation.
Ego Data FAQ
Is ego data the same as egocentric video?
No. Egocentric video is one modality. Ego data for Physical AI should synchronize video with motion, pose, force, contact, state, task context, and outcome.
Is ego data already operational data?
Not by itself. Ego data is observational task evidence. It becomes operational data after task structure, physical causality, quality, lineage, and evaluation context are compiled.
Can human ego data train robots directly?
It can support pretraining and representation learning, but embodiment differences require calibration, task schemas, action mapping, quality controls, and often robot-specific adaptation.
What formats can ArcheBase deliver?
Depending on the task and model workflow, assets can be exported with raw media and synchronized records as well as training-ready structures such as MCAP, HDF5, Zarr, and LeRobot-compatible datasets.
Start with one real human task.
See how first-person task evidence can become operational data for your Physical AI training and evaluation workflow.