Pillar 01 · Physical AI · Training Data

Robots don't lack motors. They lack experience.

We don't build robots. We build the training data that teaches them — demonstration capture, curation, simulation, and the evaluation sets that tell you whether a policy is actually ready.

Discuss a dataset See the pipeline
An engineer working hands-on with a humanoid robot rig during a demonstration capture session
CAPTURE · 60 Hz
FIG. 01 — DEMONSTRATION CAPTURE

What we provide

DATA FOR EMBODIED LEARNING
D-01

Demonstration capture

Teleoperated and human-worn collection of real manipulation episodes — synchronized video, depth, force, and joint state at control rate.

D-02

Curation and labeling

Segmentation into tasks and sub-skills, success and failure labeling, and the quality pass that removes episodes a model shouldn't learn from.

D-03

Simulation and synthetic data

Scene and asset generation, domain randomization, and synthetic episodes that cover the long tail real capture can't reach economically.

D-04

Evaluation sets and benchmarks

Held-out task suites with scoring criteria, so you can measure a policy honestly before it touches a production line.

The pipeline

CAPTURE → CURATE → TRAIN-READY → EVALUATE
Stage 01

Capture

Rigs, protocols, and trained operators collecting episodes against a defined task taxonomy.

Stage 02

Curate

Sync, segment, label, and filter. Every episode scored for quality before it enters the set.

Stage 03

Train-ready

Normalized schemas, standard action spaces, and dataset cards documenting exactly what's inside.

Stage 04

Evaluate

Held-out suites and rubrics that report where the policy succeeds, fails, and recovers.

Where the data comes from

CAPTURE ENVIRONMENTS
An industrial robot arm on an instrumented workcell inside a research lab

Capture labs

Controlled cells where a task taxonomy can be covered densely and repeatably.

Workers moving pallets down the racking aisles of a working warehouse

Partner facilities

On-site collection in real conditions: real clutter, real lighting, real time pressure.

A rendered wireframe terrain grid representing a randomized simulation scene

Simulation

Synthetic coverage of rare and unsafe cases that would be impractical to stage physically.

What ships

DELIVERABLES
Episode datasets Task-segmented manipulation episodes with synchronized modalities and quality scores PARQUET · MP4 · HDF5
Dataset cards Provenance, task taxonomy, coverage statistics, and known gaps documented per release MARKDOWN · JSON
Evaluation suites Held-out tasks with scoring rubrics and baseline results for policy comparison HARNESS + SPEC
Collection programs Standing capture operations run against your task list, delivered on a cadence ONGOING
Why data decides it

The policy is only as good as the demonstrations.

Model architectures are converging and increasingly available to everyone. What separates a demo from a deployment is coverage of the awkward cases — the misaligned part, the wet surface, the recovery after a drop. That coverage is a data problem, and it is the one we work on.

Scope a data program →