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.
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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.
Onboarding labs, robotics companies, OEMs, and research groups.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-010629cell-aINGEST81processing
ep-010630cell-dINGEST56processing
ep-010631cell-aANNOTATE74queued
ep-010632arm-03ANNOTATE59queued
ep-010633run-118NORMALIZE92queued
ep-010634cell-aSCORE60queued
ep-010635run-042INGEST78queued
14,208Episodes processed
10,681Training-ready
78.6%Avg readiness score
Raw multimodal data flows in. Structured, scored, governed datasets flow out. Less time on plumbing. More time improving models.