Exporting to training formats | Docs

An export writes a slice in one format. Exports are cached by slice, format and options, so requesting the same export twice returns the existing files.

Formats

Format	`format`	Best for	Contains
LeRobot	`lerobot`	Imitation learning, VLA fine-tuning	Episodes as Parquet plus MP4 video, `meta/` info
RLDS	`rlds`	TensorFlow and Open X-Embodiment tooling	TFRecord episodes with RLDS step structure
WebDataset	`webdataset`	Large-scale PyTorch training	Tar shards of 256 MB to 1 GB
MCAP	`mcap`	Debugging, Foxglove, Rerun, ROS 2	Aligned channels plus derived topics
OpenUSD	`usd`	Isaac Sim, Isaac Lab	Scene with Gaussian splats and collision meshes
MJCF	`mjcf`	MuJoCo	Collision meshes and a scene file

Options

Option	Type	Default	Description
`cameras`	list of strings	all	Camera channels to include
`fps`	number	source rate	Resample every stream to this rate
`resample`	`"nearest"` or `"linear"`	`"nearest"`	Method for resampling non-image streams
`action`	string	none	Channel used as the action, for example `joints/position_delta`
`frame`	string	`"rayform"`	Output convention: `rayform`, `ros`, `opencv`, `opengl`, `isaac`, `mujoco`
`quat_order`	`"wxyz"` or `"xyzw"`	`"wxyz"`	Quaternion order in the output
`image_size`	`[width, height]`	source	Resize images

Exporting with Python

Python

export = client.exports.create(
    slice_,
    format="lerobot",
    options={"cameras": ["cam0/rgb", "wrist/rgb"], "fps": 15, "action": "joints/position_delta"},
    destination="s3://my-bucket/rayform/mug-grasps/",
)
export.wait()
print(export.status, export.files[:2])

To write to your own bucket, grant Rayform's export role write access to the prefix (see Exports). Without a destination, files are kept in Rayform storage and listed with signed URLs that expire after 15 minutes.

Loading a LeRobot export

Python

from lerobot.datasets.lerobot_dataset import LeRobotDataset

ds = LeRobotDataset("rayform/mug-grasps", root="/data/rayform/mug-grasps")
print(ds.num_episodes, ds.features.keys())

Every episode's metadata carries rayform.slice, rayform.segment and rayform.segment_version, so you can trace any training sample back to its recording.

Large exports

Exports run next to your data and write in parallel. For training clusters, export to a bucket in the same region as the cluster, so each epoch reads locally.