A fleet already captures the experience. It does not become a model by itself.
Avala is fleet learning infrastructure for Physical AI. Your fleet's recordings become one 4D world, labeled once and published as versioned training releases. You train the model.
The loop
From capture to release, and back from the field.
Avala runs every step inside the loop on your fleet's own data. Training stays on your side.
Capture
MCAP, ROS, LiDAR, video. What the fleet already records.
Reconstruct
Every sensor of a sequence, together with time, in one 4D world.
Label
In that world, once. A label holds across every camera and the LiDAR.
Publish
A versioned training release on open formats. Yours to train on.
Trace
A field failure traces to the sequences and the labels behind it.
Write back: the failure becomes the next capture brief.
- Release
- Field failure
- Avala Cloud, with BYOS keeping source data in your bucket and region.
- MCAP and RRD · LiDAR, video, images, audio, and text
- Import LeRobot datasets and ROS bag camera streams through our SDK, or connect custom formats with adapters.
Proof
What is true of every release.
One 4D world
Every sensor of a sequence, reconstructed together with time.
Labeled once
A label made in that world holds for every camera and the LiDAR.
Open 4D format
MCAP and RRD in, standard formats out. No lock-in.
Customer-owned releases
Versioned and traceable. Source data stays in your bucket and region.
Never pooled
Your data trains your model. It is never mixed with another customer's.
Robots and AI are worth building only if they are good for people: the people who teach them, the people who own them, and the people they work beside.
Research & perspectives
What Physical AI teams are learning now
Technical analysis, field lessons, and practical guidance for building better data-to-model systems.
The largest market in history, and our bet on it
Every fleet should learn from its own experience. Why Avala is building the open fleet learning infrastructure for people-first AI and robotics, what we are optimizing for, and how today's work earns it.
Read more- Sep 4, 2026
Sovereign Physical AI: own the data-to-model loop, rent the rest
The shift from renting intelligence to owning it is now measurable in language models. The same four forces apply with more force in robotics, where there is no internet of robot actions and the data is the moat. What to own, what to rent, what is genuinely open to build on, and the infrastructure that makes ownership possible.
Insights - Aug 10, 2026
Four bottlenecks, three workarounds: robot learning in 2026
Robot learning's strongest 2026 papers attack memory, reasoning, dexterity, and world modeling. Three of the four work around data the field cannot collect yet.
Research
Your fleet already captures the experience.
Let it improve the model.
A demo walks the loop on real fleet data, from capture to a traced field failure.