Note

This is a Hugging Face dataset. For large datasets, ensure huggingface_hub>=1.1.3 to avoid rate limits. Learn more in the Hugging Face integration docs.

Dataset Card for Intern Pour Tactile (51 episodes)#

Intern Pour Tactile preview

This is a FiftyOne dataset with 51 samples. Each sample is one teleoperated episode of a single pouring task.

Installation#

pip install -U fiftyone

Usage#

Load from the Hub#

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

dataset = load_from_hub("Voxel51/intern-pour-lerobot-51ep")

session = fo.launch_app(dataset)

Download a snapshot and load it locally#

import fiftyone as fo
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="Voxel51/intern-pour-lerobot-51ep",
    repo_type="dataset",
    local_dir="intern-pour-lerobot-51ep",
)

dataset = fo.Dataset.from_dir(
    dataset_dir="intern-pour-lerobot-51ep",
    dataset_type=fo.types.LeRobotDataset,
    name="intern_pour_tactile",
)

session = fo.launch_app(dataset)

Dataset Details#

Dataset Description#

51 GELLO-teleoperated demonstrations of one instruction (“Pick up the small glass cup by its handle, pour the water into the large beaker, and place the cup back on the table.”) on the intern_gello_7dof_robotiq robot schema. The source is a community LeRobot v3.0 repo with two RGB views, two tactile image streams, and force/torque channels. All 51 source episodes are imported, with no success filtering (per the source README).

Shared by: cloudfan on Hugging Face
Language(s): en
License: None declared in the source repo

Dataset Sources#

Repository: https://huggingface.co/datasets/cloudfan/intern-pour-lerobot

Uses#

Direct Use#

Per the source README, training with the InternVLA A-series project (two RGB views mapped; tactile/force channels retained as auxiliary data).

Dataset Structure#

Topology. media_type: multimodal, 51 samples, one per episode, 30 fps, 649-1,179 frames (21.6-39.3 s), 44,042 frames total. Samples hold a media_reference to the per-frame parquet data and 4 video streams.

Field	FiftyOne type	Description
`media_reference`	`LeRobotEpisodeReference`	Pointers into `data/` parquet and the 4 video streams
`episode_index`	`IntField`	Episode index (0-50)
`task`	`StringField`	The single pouring instruction
`tasks`	`ListField(StringField)`	All task strings of the episode
`length`	`IntField`	Frames in the episode
`duration`	`FloatField`	Episode duration in seconds
`robot_type`	`StringField`	`intern_gello_7dof_robotiq`
`fps`	`FloatField`	30

Data behind media_reference (per-frame, from the source README):

Feature	Shape	Description
`observation.state`	(8,)	7 measured joint angles + Robotiq knuckle angle (rad)
`action`	(8,)	7 absolute GELLO joint targets + commanded gripper open fraction (0 closed, 1 open)
`observation.eef_pose`	(6,)	Original six-value pose, no coordinate conversion
`observation.wrench`	(6,)	Force/torque; 3 episodes have missing readings filled with zero
`observation.wrench_valid`	(6,)	Per-axis validity flags for `observation.wrench`
`observation.gripper_command`	(1,)	Recorded follower gripper command
`source_timestamp`	(1,) float64	Original timestamps
`observation.images.cam_high`, `cam_front`	480x640x3	RGB views (H.264 after re-encode; MJPEG in the source)
`observation.images.tactile_left_aug_diff`, `tactile_right_aug_diff`	700x400x3	Tactile touch images (H.264 after re-encode; MJPEG in the source)

Label types and why. The importer produces episode-level samples with a media_reference; numeric streams (state, action, wrench) are read through the viewer, not stored as label fields. The source has a single task, so task is constant.

dataset.info. lerobot entry with format, format_major, episode_count, imported_episode_count, skipped_episodes (empty).

Parsing decisions.

All 51 episodes imported; shard-0 alone holds only 1 episode, so the full repo (6.9 GB) was downloaded.
Metadata repair: meta/tasks.parquet stored the task text as the pandas index (task_index column only). It was rewritten with real task_index and task columns before import. No other source file was changed.
Video transcode: the source videos are MJPEG in MP4 (remuxed from original JPEG packets, per the source README), which browsers cannot decode, so they did not play in the FiftyOne App. All 204 files (51 episodes x 4 streams) were re-encoded in place with ffmpeg to H.264 (libx264, -crf 18, -g 2, yuv420p, limited range converted from full-range yuvj420p). This is a lossy re-encode of the source pixels. Frame counts, fps and timestamps are unchanged (verified per file), and video.codec / video.pix_fmt in meta/info.json were updated to h264 / yuv420p. The original MJPEG files are not part of this dataset.

Dataset Creation#

Source Data#

Data Collection and Processing#

The source README describes the original upload, before the H.264 re-encode covered under Parsing decisions. JPEG packets were remuxed into MJPEG MP4 with no resize or re-encode, and every recorded frame is kept. Timestamps are frame_index / 30, not wall-clock resampled; the capture FPS is in the source manifest. Numeric fields were compared to source values, and JPEG bytes were SHA256-checked per stream. Validation records are in meta/validation.json, meta/source_manifest.json and meta/conversion.json.

Annotations#

Annotation process#

A single natural-language instruction states the intended goal. Per the source README, it does not mean every demonstration succeeded.