38/38caught
Readiness catches faults
Euler's gate routes every injected timeline and camera fault to review (AUC 0.90 across all fault types) while auto-accepting 96% of clean episodes. On a real corpus, only 3% needs a human.
∑
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 labs, companies, startups, and research groups building physical & multimodal AI.Raw multimodal data
from the physical world
Σuler
data-readiness layer
Training-ready datasets
for embodied (VLA) + spatial models
Built for teams advancing the physical-AI frontier.
Not a labeling vendor. Not a fleet dashboard. The data-readiness layer for teams whose models live in the real world.
Where it runs
- Robot learning and policy teamsBehavior cloning, imitation, RL, VLA fine-tuning
- Humanoid and manipulation OEMsBipeds, dexterous arms, mobile manipulators
- Robotics foundation-model labsPretraining and post-training data pipelines
- Industrial automation at scaleWarehouse, logistics, factory cells
Also fits autonomous vehicle stacks, spatial intelligence platforms, and research groups.
What it ingests
Containers
- MCAP
- ROS 1 and ROS 2 bags
- Parquet and HDF5
- MP4 and image sequences
Modalities
- RGB and depth video
- LiDAR and point clouds
- Joint state, IMU, force / torque
- Language and teleop annotations
Scale
- From single robots to fleet ingest
- Connect your own object storage
- Hosted preview environment
System output. What Euler produces.
ep-011303cell-dSCORE61processing
ep-011304arm-03ANNOTATE89processing
ep-011305fleet-23NORMALIZE88queued
ep-011306run-042PACKAGE77✓ ready
ep-011307run-042SCORE85queued
ep-011308arm-08SCORE88queued
ep-011309fleet-23INGEST71queued
14,208Episodes processed
10,681Training-ready
78.6%Avg readiness score
Proof that Σuler makes robot data train better
Every number below comes from real datasets run end to end through the product, from readiness scoring to annotation, curation, and export, and is reproducible through the same upload-and-run path you would use.
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.
Your ML team writes one-off parsers. They argue over what counts as a usable episode. The training queue idles. Your model ships late, weaker than it should be.
Raw multimodal data flows in. Structured, scored, governed datasets flow out. Less time on plumbing. More time improving models.