⚠️ Robot policy — needs a matching robot to actuate. This is lerobot/VLA-JEPA-Pretrain converted to an Apple Core AI .aimodel. Its output is a normalized action chunk for droid_pretrain. Run it on any other robot, or with mismatched calibration / normalization stats, and it emits floats that look valid but actuate garbage — the most dangerous silent-failure mode. It is a base checkpoint: fine-tune on your robot's data before expecting useful behavior.

An Apple Core AI conversion of lerobot/VLA-JEPA-Pretrain — a robot policy that maps images + proprioceptive state (+ a language instruction, when the model uses one) to a continuous action chunk (flow_matching sampler). Produced by coreai-fabric and indexed by coreai-catalog.

Model facts

Field	Value
Parameters	3B
Architecture	transformer
Capabilities	vision-language-action, robotics
Embodiment	droid_pretrain
Sampling	flow_matching (4-step)
Quantization / precision	none / bfloat16
On-disk size	3.2 GB
Asset kind	split-export policy (qwen_context, action_denoise_step) + norm_stats
assetVersion	2.0

Use it — this needs host code you supply

A policy is not a chat model: there is no stock high-level Swift runtime for it. The bundle is the split-export shape — an encode graph (run once per observation) + a denoise_step graph (the host drives it num_steps times) + norm_stats.json (un-normalization). You supply the host loop (the N-step sampler + un-normalization) in Swift. Recommended integration: keep LeRobot's Python RobotClient for the servos/cameras/calibration, and run inference on-device — see the io_contract in the catalog for the exact tensors.

pip install coreai-catalog && coreai-catalog install vla-jepa-pretrain

Requirements

Deployment: macOS 27.0+ / iOS 27.0+, Xcode 27+. The asset serializes with minimum_os v27, so the on-device Swift runtime requires macOS/iOS 27+. A Mac on macOS 26 can convert and inspect it but not run it on-device.
Apple Silicon. A matching robot to actuate (see the banner).

Verification (action parity)

Gate A (structure): passed — the bundle's layout + metadata were validated on real hardware; the graphs load.
Gate B — action_parity: 100.0% min chunk-cosine vs the fp16 reference over 8 frames of recorded episodes, 4-step, fixed-noise (measured on Apple Silicon).
This certifies the export is numerically faithful to the source policy — it does NOT certify real-world task success, embodiment transfer, or closed-loop stability. Closed-loop success-rate eval (LIBERO/ManiSkill): not_run (a separate future gate). Reproduce with coreai-fabric verify.

Provenance

Field	Value
Base model	lerobot/VLA-JEPA-Pretrain @ `e946c3e5b538d760f4b4ff239d1b1c12090c041d`
Converted by	`models/vla_jepa/export.py` (version not reported)
Recipe	vla-jepa-pretrain (recipe_source: fabric)
Precision / quantization	bfloat16 / none
Conversion date	2026-07-07

Machine-readable, in this repo: parity-report.json (gate results) · reproduce-manifest.json · LICENSE (upstream terms).

License and attribution

Weights licensed apache-2.0 — see the bundled LICENSE. This artifact is a converted derivative of the base policy: its weights were converted to Apple Core AI format. The conversion itself is community work.

The on-device Core AI ecosystem

coreai-fabric — the reproducible recipe → .aimodel pipeline that produced this asset.
coreai-catalog — the index of Core AI models across the community, with provenance and integration snippets.
apple/coreai-models — Apple's official exporters and runtimes.
LeRobot — the upstream robotics ecosystem.

Not affiliated with Apple

Community conversion. Not produced, hosted, or endorsed by Apple. Apple and Core AI are trademarks of Apple Inc., used here only to describe the target runtime/format. This is an independent community conversion.

kevinqz/VLA-JEPA-Pretrain-CoreAI

VLA-JEPA Pretrain (fabric)