Voxel51/tavis-head-gr1t2-800ep

This dataset was created using LeRobot and is presented here as a FiftyOne dataset.

Installation

pip install -U fiftyone

Usage

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

dataset = load_from_hub("Voxel51/tavis-head-gr1t2-800ep")
session = fo.launch_app(dataset)

Dataset Card for TAVIS Head GR1T2 (800-episode FiftyOne export)

Dataset Details

Dataset Description

TAVIS Head GR1T2 is part of the (TAVIS) Torso Active-Vision Imitation Suite, a benchmark of teleoperated bimanual manipulation episodes collected on a humanoid platform. Each episode pairs four synchronized camera streams (a head-mounted active vision camera, a fixed scene camera, and two wrist cameras) with 60 Hz proprioceptive state, dual end-effector pose, and 19-dimensional action vectors, spanning 5 task families: cluttered pick-and-place, conditional picking, multi-shelf scanning, and wait-then-act behaviors. This card covers a full FiftyOne re-export of the source LeRobot v3.0 repository: all 800 episodes / 267,046 frames.

• Curated by: tavis-benchmark (Hugging Face organization; individual authors not disclosed in the source repository)
• Shared by: tavis-benchmark
• License: cc-by-4.0

Dataset Sources

• Repository: https://huggingface.co/datasets/tavis-benchmark/tavis-head-gr1t2

Uses

Direct Use

Training and evaluating imitation-learning / vision-language-action policies for bimanual humanoid manipulation, benchmarking active-vision (moving head camera) strategies against fixed-camera baselines, and studying multi-task generalization across pick, conditional-pick, multi-shelf, and wait-then-act behaviors. Two pi0-based policies fine-tuned on this dataset are published by the same organization (tavis-benchmark/pi0-tavis-head-gr1t2-fixedcam, tavis-benchmark/pi0-tavis-head-gr1t2-headcam).

Out-of-Scope Use

Not intended for deployment on physical robots without further safety validation. Not annotated for object detection, segmentation, or any non-robotics computer-vision task.

Dataset Structure

Fields

Field	Type	Description
media_reference	MediaReferenceField	Pointer to the episode's synchronized video/data streams
episode_index	IntField	Episode index (re-indexed 0..799 on export)
task	StringField	Free-text task name for the episode (frame-0 label; see below)
tasks	ListField(StringField)	All task labels associated with the episode
length	IntField	Number of frames in the episode (range 173–605)
duration	FloatField	Episode duration in seconds (range 2.88–10.08)
robot_type	StringField	Robot identifier; not populated in the source metadata
fps	FloatField	Frame rate, 60.0 for every episode

Label types and why

task is a StringField, not a Classification, because the source stores it as free-text per episode (one task name per episode in this repo) rather than a per-frame categorical label. Use tasks if you need the list form, and count_values("task") to tabulate task frequency (see below).

Per-frame streams (from meta/info.json, not FiftyOne sample fields)

Shown in the FiftyOne App's State & Action / Streams tabs, not as top-level sample fields:

Feature	dtype	Shape	Notes
observation.images.OBS_HEAD	video	[480, 640, 3]	AV1, yuv420p, 60 fps, head-mounted active-vision camera
observation.images.OBS_FIXED	video	[480, 640, 3]	AV1, yuv420p, 60 fps, fixed scene camera
observation.images.OBS_WRIST_LEFT	video	[480, 640, 3]	AV1, yuv420p, 60 fps
observation.images.OBS_WRIST_RIGHT	video	[480, 640, 3]	AV1, yuv420p, 60 fps
action	float32	[19]	Environment/joint actions
observation.state	float32	[44]	Full proprioceptive state
observation.left_eef_pos	float32	[3]	Left end-effector position
observation.left_eef_quat	float32	[4]	Left end-effector orientation (quaternion)
observation.right_eef_pos	float32	[3]	Right end-effector position
observation.right_eef_quat	float32	[4]	Right end-effector orientation (quaternion)
language_instruction	string	[1]	Natural-language instruction for the episode
timestamp, frame_index, episode_index, index, task_index	float32/int64	[1]	Standard LeRobot bookkeeping columns

dataset.info contents

ds.info["lerobot"] records the original LeRobot metadata (codebase version, fps, feature declarations) and skipped_episodes (empty — all 800 episodes imported cleanly).

Parsing decisions

• Full dataset, not a subset. All 800 episodes from the source repository were imported; this export is a re-encoding of the complete dataset rather than a sampled subset, so episode indices are unchanged in content (0-based, contiguous) and no episodes were excluded.
• meta/tasks.parquet repair. The source file stored task names as the pandas DataFrame index (columns ['task_index', '__index_level_0__']) instead of the task_index + task columns the LeRobot v3 reader and FiftyOne's export path require. Repaired locally by resetting the index into a task column before ingest; no task content was changed, only its column layout.
• meta/episodes data/file_index repair. The source episode metadata declared data/file_index values 0–3 (implying 4 data shard files), but the repo only ships a single data/chunk-000/file-000.parquet, which in fact contains all 267,046 rows for all 800 episodes. The stale file_index values caused export to look for nonexistent file-001.parquet/file-002.parquet/file-003.parquet. Repaired locally by setting data/chunk_index / data/file_index to 0 for every episode; no row data was moved or altered, only the shard pointer.
• No modalities excluded. All 4 declared video streams and all declared low-dimensional features (dtype: "video" / numeric) are supported by FiftyOne's LeRobot importer and are all present in this export.
• Re-export. As with any FiftyOne → LeRobot push, episode indices were re-verified against 0..799 and per-episode/global stats were recomputed by the exporter; only the shard files referenced by the imported episodes are included (here: all shards, since all episodes were imported).
• robot_type is null in the source meta/info.json and is therefore empty for every sample; the humanoid platform is implied by the eef/state field layout (dual arm, 44-dim state, 19-dim action) but not named in the source metadata.

Dataset Creation

Curation Rationale

Collected as part of the TAVIS benchmark to study the effect of active (head-mounted, moving) vision versus fixed cameras on bimanual manipulation policies, across a mixture of pick-and-place, conditional, multi-object, and waiting behaviors.

Source Data

Data Collection and Processing

Episodes were recorded via teleoperation on a bimanual humanoid platform at 60 fps, with 4 synchronized AV1-encoded camera streams and paired proprioceptive/action logs, stored in LeRobot v3.0 format (single Parquet data shard, video streams sharded per camera). Task distribution: ClutterPickLiftTask and MultiShelfScanTask each 250 episodes; ClutterPickCubeTask, ConditionalPickTask, and WaitThenActTask each 100 episodes (800 total).

Annotations

Annotation process

No manual annotations beyond the task label and language instruction recorded at collection time.

Personal and Sensitive Information

No personal or sensitive information is expected; episodes depict a robot manipulating objects on a workbench, not people.

More Information

Codec note: all video streams use AV1 (yuv420p), which decodes reliably in Chromium-based browsers; playback support may vary in other browsers.

Dataset Card Authors

Harpreet Sahota

Dataset Card Contact

Harpreet Sahota