Synchronizing Video, Motion, Force, Contact, and Outcome
Why multimodal task evidence needs a shared clock, calibration, event alignment, and quality controls before it can support Physical AI.
Synchronization creates the task episode
Physical AI data arrives at different frequencies and through different devices. Video may run at 30 or 60 Hz, pose tracking at another rate, force sensing at hundreds of samples per second, and task outcomes as sparse events. Without a shared time model, these streams are merely files captured near one another.
Synchronization turns them into a task episode.
Start with a shared task clock
Every device should map its local timestamps to a shared task clock. The system must record clock source, offset, drift, synchronization method, and uncertainty.
This is especially important for contact-rich tasks. A 50–100 ms error may connect the wrong video frame to a force spike or make an action appear to precede the contact it actually caused.
Preserve raw timestamps
Do not overwrite device timestamps after alignment. Keep both the original timestamp and the normalized task time. Raw timing supports audits, recalibration, and improved alignment algorithms later.
The episode manifest should also record dropped frames, buffering, packet loss, and periods where timing confidence falls below a threshold.
Calibrate space as well as time
Temporal alignment is not sufficient. Cameras, wearable devices, tools, force sensors, and robot frames must share known spatial relationships.
Store intrinsics, extrinsics, coordinate-frame names, calibration targets, calibration dates, and revision identifiers. If a sensor moves during capture, treat that as a new configuration rather than silently reusing old calibration.
Align on physical events
Contact events provide useful anchors. A visible tool-object impact, force spike, tactile transition, robot-state change, or synchronized trigger can validate clock alignment across modalities.
Event alignment should not replace hardware or protocol synchronization, but it can reveal residual offsets and drift.
Model different sampling rates explicitly
Avoid forcing every signal into a single rate too early. Resampling can hide high-frequency force events or create false precision in slower streams.
A better approach preserves native samples and exposes aligned views for downstream tasks. Training exports can then choose interpolation, windows, or event-centered slices appropriate to the model.
Quality gates for synchronized episodes
Before an episode enters Data Compilation, verify:
- clock offset and drift remain within policy;
- required streams are present;
- calibration versions are valid;
- frame loss and sensor gaps are reported;
- contact events align within tolerance;
- task start, end, and outcome are consistent;
- the episode can be replayed from its manifest.
The Task Interface owns capture-time synchronization. Physical AI DBMS preserves timing, calibration, quality, and lineage as the corpus evolves.
Synchronization is a semantic requirement
The purpose of synchronization is not technical neatness. It is what allows a model to learn that an action produced a contact, that contact changed state, and that the resulting state led to success, failure, or recovery.
That causal chain begins with trustworthy time.
Explore the complete Ego Data for Physical AI topic, or request a data demo for a specific capture workflow.