Multimodal data engineering
Ingest robot logs and world data, align clocks and schemas, and turn long recordings into consistent episodes across every modality.
∑
The data fabric
beneath Physical AI.
We turn raw robot logs and multimodal world data into structured, scored, searchable training-ready datasets for VLA, robot policy, and spatial intelligence teams.
For teams building physical and multimodal models, deploying autonomous systems across embodiments, or turning real-world data into trusted assets.
01 / Perceive
Raw multimodal data
from the physical world
align
02 / Structure
Σuler
data-readiness layer
prove
03 / Activate
Training-ready datasets
for embodied (VLA) + spatial models
The data layer between the physical world and the model.
Euler turns fragmented sensor streams into one inspectable data system, then carries that system through engineering, certification, annotation, curation, and delivery.
What Euler ingests
A live data bus, not a file-format checklist.
Formats, sensors, annotations, and scale stay connected as one multimodal record.
FMT▦
Containers and recordings
Native robot logs, dataset stores, and media enter without a one-off rewrite.
MCAPROS 1 / ROS 2ParquetHDF5MP4image sequences
SIG◉
Physical and semantic signals
Multi-rate streams stay linked as Euler aligns the physical event they describe.
RGBdepthLiDARjoint stateIMUforce / torqueaudiolanguage
SCL∞
From one run to a fleet
Start with a measured dataset and grow into governed, repeatable data operations.
single episodeslarge corporafleet ingestyour object storehosted access
System output. What Euler produces.
ep-014208fleet-17PACKAGE92✓ ready
ep-014207arm-03SCORE86processing
ep-014206run-042ANNOTATE77queued
ep-014205cell-aNORMALIZE69queued
ep-014204arm-08PACKAGE94✓ ready
ep-014203fleet-23FILTER81queued
ep-014202run-118INGEST63queued
14,208Episodes processed
10,681Training-ready
78.6%Avg readiness score
One platform, three ways into the data loop.
Physical AI is the sharp edge. The same data layer also serves multimodal model teams, autonomous systems across embodiments, and partners who collect the raw world data those systems depend on.
01Train
Physical AI and multimodal model builders
Teams training world models, VLMs, VLA systems, spatial intelligence, and robot policies need raw multimodal data they can trust before it reaches a model.
VLMVLAworld modelsspatial AI
02Deploy
Autonomous embodiment deployment
Teams operating real-world autonomy need better feedback data from every deployment, across humanoids, vehicles, drones, robots, and industrial systems.
humanoidsautonomous vehiclesdronesindustrial bots
03Supply
Data collection and capture partners
Capture companies, labeling platforms, and data vendors can turn raw multimodal collections into inspectable assets that buyers can evaluate and use.
capture rigsdata vendorslabeling platformsresearch corpora
Whether you train the model, deploy the embodiment, or supply the data, Euler turns raw multimodal evidence into something your next decision can trust.
Move from data drag to deployment momentum.
Euler connects collection, quality evidence, training slices, and deployment feedback in one loop. Teams spend less effort proving the same data twice and more effort improving the model on hardware.
Without EulerA fragmented data operation
- Collection stays siloed across vendors, robots, and teams.
- Quality lives in spreadsheets and reviewer judgment, without a shared evidence chain.
- Parsers, relabeling, and broad retraining consume engineering and compute.
- Hardware failures cannot be traced cleanly back to the episode or data decision.
Time
Model iteration waits on cleanup, review, and handoffs.
Spend
Outcome: delayed models and hardware deployment.Budget leaks into rework, duplicate labeling, and low-value compute.
With ΣulerAn inspectable model improvement loop
- One multimodal record connects capture, readiness, labels, curation, and export.
- Every quality verdict keeps policy, evidence, and provenance inspectable.
- Clean data flows through while uncertain data moves to targeted review.
- Deployment failures become searchable slices for retraining and redeployment.
Time
A shorter path from raw logs to a trainable, debuggable slice.
Spend
Outcome: more model iterations and better hardware deployment.Engineering, labeling, and compute focus on data that can change the model.
Proof across readiness, annotation, curation, and search
These are measured results from public benchmarks, controlled fault tests, and recent anonymous pilots. Each result keeps its method and evidence close enough to inspect.
13/14usable
A real dataset audited before training
A recent multimodal pilot ran 392 per-recording checks with 100% required-evidence coverage. One recording was held back for a duplicated timestamp and a 15-frame stream mismatch.
38/38caught
Controlled readiness faults caught
Euler routes every injected timeline and camera fault to review, with AUC 0.90 across fault types while auto-accepting 96% of clean episodes.
0.723precision
The adopted detector won the audit
Holdout precision 0.723 and recall 0.567 on public ground truth. The production detector was chosen by measurement, not preference.
89%
Auto-labels match humans
Agreement between Euler's zero-shot success verdict and DROID's human labels, with zero labeling hours.
11.2×
Curation generalizes
Distance between robot datasets versus within one, across four embodiments, with perfect nearest-neighbor purity and no per-dataset tuning. Curation spans all four.
0.87hit@1
Search finds it first
Hybrid retrieval puts the right episode at rank one 87% of the time, over real episodes with relevance taken from human labels.
Start with context, not a sales script
Tell us what you are building.
Share the data, model, deployment, or partnership you have in mind. We will map the most useful place to start with Euler.
Partners and memberships
Connected to the ecosystems shaping physical AI.
Programs and technical ecosystems that strengthen the work behind Euler. Membership does not imply endorsement.
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