The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
tasks: struct<avoiding: struct<episodes: int64, frames: int64, clips: int64, train_clips: int64, loader_sha (... 1821 chars omitted)
child 0, avoiding: struct<episodes: int64, frames: int64, clips: int64, train_clips: int64, loader_shapes: struct<laten (... 127 chars omitted)
child 0, episodes: int64
child 1, frames: int64
child 2, clips: int64
child 3, train_clips: int64
child 4, loader_shapes: struct<latents: list<item: int64>, text_emb: list<item: int64>, actions: list<item: int64>, actions_ (... 24 chars omitted)
child 0, latents: list<item: int64>
child 0, item: int64
child 1, text_emb: list<item: int64>
child 0, item: int64
child 2, actions: list<item: int64>
child 0, item: int64
child 3, actions_mask: list<item: int64>
child 0, item: int64
child 5, passed: bool
child 1, pushing: struct<episodes: int64, frames: int64, clips: int64, train_clips: int64, loader_shapes: struct<laten (... 127 chars omitted)
child 0, episodes: int64
child 1, frames: int64
child 2, clips: int64
child 3, train_clips: int64
child 4, loader_shapes: struct<latents: list<item: int64>, text_emb: list<item: int64>, actions: list<item: int64>, actions_ (... 24 chars omitted)
child 0, latents: list<item: int64>
child 0, item: int64
child 1, text_emb: list<item: int64>
child 0, item: int64
...
child 0, item: int64
child 3, actions_mask: list<item: int64>
child 0, item: int64
child 5, passed: bool
child 7, stacking: struct<episodes: int64, frames: int64, clips: int64, train_clips: int64, loader_shapes: struct<laten (... 127 chars omitted)
child 0, episodes: int64
child 1, frames: int64
child 2, clips: int64
child 3, train_clips: int64
child 4, loader_shapes: struct<latents: list<item: int64>, text_emb: list<item: int64>, actions: list<item: int64>, actions_ (... 24 chars omitted)
child 0, latents: list<item: int64>
child 0, item: int64
child 1, text_emb: list<item: int64>
child 0, item: int64
child 2, actions: list<item: int64>
child 0, item: int64
child 3, actions_mask: list<item: int64>
child 0, item: int64
child 5, passed: bool
total_episodes: int64
total_frames: int64
total_clips: int64
elapsed_s: double
normalization: string
passed: bool
config_name: string
sample_shapes: struct<latents: list<item: int64>, text_emb: list<item: int64>, actions: list<item: int64>, actions_ (... 24 chars omitted)
child 0, latents: list<item: int64>
child 0, item: int64
child 1, text_emb: list<item: int64>
child 0, item: int64
child 2, actions: list<item: int64>
child 0, item: int64
child 3, actions_mask: list<item: int64>
child 0, item: int64
train_clips: int64
dataset_count: int64
to
{'dataset_count': Value('int64'), 'train_clips': Value('int64'), 'config_name': Value('string'), 'sample_shapes': {'latents': List(Value('int64')), 'text_emb': List(Value('int64')), 'actions': List(Value('int64')), 'actions_mask': List(Value('int64'))}, 'passed': Value('bool')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
tasks: struct<avoiding: struct<episodes: int64, frames: int64, clips: int64, train_clips: int64, loader_sha (... 1821 chars omitted)
child 0, avoiding: struct<episodes: int64, frames: int64, clips: int64, train_clips: int64, loader_shapes: struct<laten (... 127 chars omitted)
child 0, episodes: int64
child 1, frames: int64
child 2, clips: int64
child 3, train_clips: int64
child 4, loader_shapes: struct<latents: list<item: int64>, text_emb: list<item: int64>, actions: list<item: int64>, actions_ (... 24 chars omitted)
child 0, latents: list<item: int64>
child 0, item: int64
child 1, text_emb: list<item: int64>
child 0, item: int64
child 2, actions: list<item: int64>
child 0, item: int64
child 3, actions_mask: list<item: int64>
child 0, item: int64
child 5, passed: bool
child 1, pushing: struct<episodes: int64, frames: int64, clips: int64, train_clips: int64, loader_shapes: struct<laten (... 127 chars omitted)
child 0, episodes: int64
child 1, frames: int64
child 2, clips: int64
child 3, train_clips: int64
child 4, loader_shapes: struct<latents: list<item: int64>, text_emb: list<item: int64>, actions: list<item: int64>, actions_ (... 24 chars omitted)
child 0, latents: list<item: int64>
child 0, item: int64
child 1, text_emb: list<item: int64>
child 0, item: int64
...
child 0, item: int64
child 3, actions_mask: list<item: int64>
child 0, item: int64
child 5, passed: bool
child 7, stacking: struct<episodes: int64, frames: int64, clips: int64, train_clips: int64, loader_shapes: struct<laten (... 127 chars omitted)
child 0, episodes: int64
child 1, frames: int64
child 2, clips: int64
child 3, train_clips: int64
child 4, loader_shapes: struct<latents: list<item: int64>, text_emb: list<item: int64>, actions: list<item: int64>, actions_ (... 24 chars omitted)
child 0, latents: list<item: int64>
child 0, item: int64
child 1, text_emb: list<item: int64>
child 0, item: int64
child 2, actions: list<item: int64>
child 0, item: int64
child 3, actions_mask: list<item: int64>
child 0, item: int64
child 5, passed: bool
total_episodes: int64
total_frames: int64
total_clips: int64
elapsed_s: double
normalization: string
passed: bool
config_name: string
sample_shapes: struct<latents: list<item: int64>, text_emb: list<item: int64>, actions: list<item: int64>, actions_ (... 24 chars omitted)
child 0, latents: list<item: int64>
child 0, item: int64
child 1, text_emb: list<item: int64>
child 0, item: int64
child 2, actions: list<item: int64>
child 0, item: int64
child 3, actions_mask: list<item: int64>
child 0, item: int64
train_clips: int64
dataset_count: int64
to
{'dataset_count': Value('int64'), 'train_clips': Value('int64'), 'config_name': Value('string'), 'sample_shapes': {'latents': List(Value('int64')), 'text_emb': List(Value('int64')), 'actions': List(Value('int64')), 'actions_mask': List(Value('int64'))}, 'passed': Value('bool')}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
dataset_count int64 | train_clips int64 | config_name string | sample_shapes dict | passed bool |
|---|---|---|---|---|
8 | 25,195 | d3il_train | {
"latents": [
48,
13,
16,
32
],
"text_emb": [
512,
4096
],
"actions": [
30,
13,
12,
1
],
"actions_mask": [
30,
13,
12,
1
]
} | true |
D3IL Vision + LeRobot / LingBot-VA
A local derivative of the D3IL demonstrations with two-camera vision coverage across all eight task subsets, LeRobot v2.1 trajectories, and precomputed LingBot-VA video/text features.
Upload status: See UPLOAD_STATUS.json. This repository is complete only when its status is complete. Files may appear incrementally during the initial upload.
Contents
| Subset | Episodes | Valid transitions | Latent clips |
|---|---|---|---|
| avoiding | 96 | 7,305 | 96 |
| pushing | 1,999 | 463,157 | 4,086 |
| inserting | 794 | 786,129 | 5,846 |
| aligning | 1,000 | 193,881 | 1,934 |
| sorting_2 | 600 | 112,128 | 1,135 |
| sorting_4 | 1,054 | 352,552 | 2,972 |
| sorting_6 | 1,661 | 846,405 | 6,706 |
| stacking | 1,094 | 685,270 | 5,298 |
| Total | 8,298 | 3,446,827 | 28,073 |
There are 25,195 train clips and 2,878 eval clips, split by episode. The release contains 8,298 Parquet trajectories, 16,596 MP4 videos, and 56,146 per-camera latent files.
lerobot/<subset>/:meta/,data/,videos/,latents/, and per-subset configuration.lerobot/lingbot_joint_config.json: shared action normalization and camera/channel mapping.text_embeddings/: task and empty-prompt embeddings.raw_vision/<subset>/part-*.tar: lossless archives of the complete original-resolution state, images, and other available rollout records. Tar members start withraw_vision/<subset>/; extract into the snapshot root.raw_vision/<subset>/shards.json: archive member counts, source byte counts, and SHA256 hashes.reports/,provenance/,licenses/: validation results, generation records, source snapshots, and upstream notices.tools/prepare_local.py: set downloaded configuration paths to the current local directory.
Python environments, temporary pilot data, training checkpoints, account credentials, and upload-worker state are excluded.
Instructions
Each subset uses a single goal-level instruction, without prescribing a route, object order, or stacking order.
| Subset | Instruction |
|---|---|
| avoiding | Move the end effector past the obstacle field to the goal region without touching any obstacle. |
| pushing | Place one block in each of the two target regions. |
| inserting | Place all three colored blocks in their corresponding target slots. |
| aligning | Align the block with the target position and orientation. |
| sorting_2 / sorting_4 / sorting_6 | Place all red blocks in the red target region and all blue blocks in the blue target region. |
| stacking | Stack all three colored blocks into a single tower in the target region. |
Generation and action semantics
Avoiding, Pushing, and Inserting images were generated by restoring logged robot/object states and rendering external and wrist cameras at 256×256. This is state restoration and rendering, not a fresh physical execution of each demonstration. Original terminal-state failures (one Pushing and six Inserting episodes) were excluded. Source-native vision for Aligning, Sorting, and Stacking is retained at 96×96; VAE inputs are resized to 256×256.
A source rollout with T observations yields T−1 valid transitions. Row t pairs the observation at t with the next logged desired joint positions and next observed gripper width. The terminal observation remains in the raw rollout. action and observation.state each have eight float32 channels: seven joint positions in radians and gripper total width in meters. Gripper width is a proxy target, not a recovered original gripper control command; non-grasping records without width use a documented 0.04 m open-gripper assumption. Missing Cartesian targets are not fabricated.
For LingBot-VA, the eight action channels map to indices 14–20 and 28 in its 30-dimensional action layout. Unused channels and initial synthetic action padding must be masked. This is a joint-target policy dataset; use a matching joint/gripper controller when evaluating.
Most tasks have original dt=0.035 s; Stacking has dt=0.03 s. Video sampling stride for VAE encoding is 3, and action_per_frame=12. Video latents use Wan2.2 VAE posterior mode with checkpoint normalization; text features use UMT5. The two camera keys are observation.images.bp_cam and observation.images.wrist_cam. Latents have 48 channels at 16×16 per view; text embeddings have shape 512×4096. Segments can overlap near episode ends and can have different temporal lengths. The validated native training configuration uses batch_size=1 per device.
The 1%/99% action normalization quantiles are shared across subsets and computed from training data only. The custom LingBot-VA loader used for validation honors dataset_split and mask_initial_action_padding; an unmodified upstream loader may not honor these optional fields.
Download
Download only the converted training data:
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="Zeus-Omni/D3IL-Vision",
repo_type="dataset",
local_dir="d3il_vision",
allow_patterns=["lerobot/**", "text_embeddings/**", "tools/**", "README.md", "reports/**", "licenses/**"],
)
Then run:
python d3il_vision/tools/prepare_local.py --root d3il_vision
For the complete source vision release, omit allow_patterns above. Check UPLOAD_STATUS.json and each shards.json before relying on completeness. Extract each raw tar into the snapshot root, for example:
tar -xf d3il_vision/raw_vision/avoiding/part-00000.tar -C d3il_vision
Each lerobot/<subset> is an individual LeRobot v2.1 dataset. The outer lerobot directory is a collection for LingBot-VA's multi-dataset loader, not a single LeRobot dataset. Generation provenance can retain original local source paths as audit references; they are not needed for training from the converted data.
Attribution
Original demonstrations and environments: ALRhub/D3IL, commit 1d9c71850d4fc1477cee17b557c8b97d70b13071. The upstream software MIT notice is preserved in licenses/D3IL-MIT.txt.
Video/action model and data format: Robbyant/LingBot-VA. The generation environment used local lingbot-va-posttrain-robotwin encoder assets. Model weights are not included in this dataset repository. See provenance/ for the local conversion scripts and dependency versions.
This is a derived dataset release, not an official upstream D3IL or LingBot-VA release. Format/load validation does not establish closed-loop policy success or physical validity of an entire restored trajectory.
- Downloads last month
- 314