Physical AI · Demonstration data

Expert human work, packaged as training data.

Seneca Trace operates capture infrastructure inside active manufacturing lines, converting expert demonstrations into millimeter-precision datasets for robot foundation models, VLA policies, and imitation learning at scale.

Request early access Watch exemplars ↓

$5T

Projected humanoid robotics market by 2050. Source: Morgan Stanley

2.1M

Unfilled US manufacturing jobs projected by 2030. Source: Deloitte

<1%

Of robot training data comes from real industrial environments

10,000×

Data gap between language models and embodied AI. The scaling bottleneck

00 / Exemplars

Egocentric work. Real factories.

Public head-mounted footage from industrial and workplace tasks on Hugging Face. Used here as domain references only. Seneca Trace captures partnered plant processes under NDA. Proprietary collects coming soon.

Exemplar · Ego

Packaging line

First-person packaging workflow. High hand visibility, repeated micro-skills, the kind of industrial motion policies need.

View on Hugging Face →

Exemplar · Ego

Metal fabrication

Egocentric metal fab task in the wild. Contact-rich tool use and precision hand-object interaction on a live shop floor.

View on Hugging Face →

Exemplar · Factory

Egocentric-10K · Factory

Head-mounted clip from a real factory worker (Build AI Egocentric-10K subset). In-the-wild industrial demonstration density.

View Egocentric-10K →

01 / The bottleneck

Scaling laws hit robotics.
Data is the constraint.

Foundation models for manipulation are compute-rich and data-poor. Simulation covers the sim-to-real gap only so far. The frontier is real-world demonstration data from environments that actually matter economically.

/ 001

Not enough trajectories

VLA and imitation learning need thousands of real human demonstrations. For millimeter-precision industrial tasks, that data barely exists on the market.

/ 002

Datasets that don't fit

What you find wasn't recorded for your embodiment, sensors, or environment. You burn cycles adapting data instead of training.

/ 003

Collection is slow & costly

Standing up capture infrastructure inside a factory takes weeks, people, and capital. Every iteration delays deployment.

02 / How it works

Two sides. One bridge.

Supply / The factory

Partner production lines

We install our capture kit on active lines under NDA and revenue share. Expert operators keep working while we record the mastery: synced RGB-D, egocentric video, and process metadata.

→ Zero disruption to production throughput
→ Data rights, consent chain, and IP protection by contract
→ Revenue share on every license, creating a data flywheel for the plant

Demand / The lab

Training-ready datasets

QC'd, segmented, and annotated demonstrations exported in the format your stack already consumes. Off-the-shelf packages or bespoke collection built around your tasks.

→ LeRobot / HDF5 / ROS 2 bags
→ Skill segments + success labels
→ Recurring collects on your training cycle

03 / The data

Real production lines.
Not staged lab demos.

Ecologically valid demonstrations from one of North America's densest manufacturing corridors: the nearshoring belt supplying the US automotive and electronics supply chain.

Environments	Automotive & electronics assembly · metalworking · packing. North American nearshoring corridor
Signals	RGB-D multi-view · egocentric video · hardware-synced timestamps · optional wrist IMU
Annotations	Skill segmentation · success / failure labels · operator & process versioning · scene calibration
Formats	LeRobot · HDF5 · ROS 2 bags · custom pipelines on request
Licensing	Per-hour or per-trajectory · exclusive collects available · full chain of consent

Seneca taught a generation through letters, knowledge that traveled without the teacher. Our datasets are those letters. Robots are the students.

04 / Early access

Build with us.

Factory, robotics lab, or integrator? Leave your email and we'll reach out about pilot deployments and dataset access.

or write us: hello@senecatrace.com