The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ArrowInvalid
Message: Float value 3.734885 was truncated converting to int64
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 289, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 124, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2224, in cast_table_to_schema
cast_array_to_feature(
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2052, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1797, in wrapper
return func(array, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2002, in cast_array_to_feature
_c(array.field(name) if name in array_fields else null_array, subfeature)
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1797, in wrapper
return func(array, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2086, in cast_array_to_feature
return array_cast(
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1797, in wrapper
return func(array, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1949, in array_cast
return array.cast(pa_type)
^^^^^^^^^^^^^^^^^^^
File "pyarrow/array.pxi", line 1135, in pyarrow.lib.Array.cast
File "/usr/local/lib/python3.12/site-packages/pyarrow/compute.py", line 412, in cast
return call_function("cast", [arr], options, memory_pool)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/_compute.pyx", line 604, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 399, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Float value 3.734885 was truncated converting to int64Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
EgoGrasp
EgoGrasp is a crowdsourced egocentric video dataset of human grasping interactions, built for robotics imitation learning. Each clip captures a single grasp action filmed from a first-person perspective using a smartphone, covering 620+ unique everyday object categories.
What's Included Here
This repository contains a sample of 10 annotated clips from the full EgoGrasp dataset. The sample is intended to help researchers evaluate data quality, annotation depth, and compatibility with their pipelines before requesting access to the full collection.
To request access to the full dataset (1,800+ clips, 620+ object categories), visit robox.to.
Dataset Summary
- Sample clips (this repo): 10
- Full dataset: 1,800+ clips across 620+ object categories
- Perspective: First-person (egocentric), smartphone-captured
- Source: Crowdsourced via the RoboX mobile app
- Annotations: Multi-pass pipeline including hand keypoints, object bounding boxes and tracking, action segmentation, and spatial context labels
| Property | Value |
|---|---|
| Total clips | 10 |
| Total duration | 2 min (~0.0 hours) |
| Contributors | 2 (anonymized) |
| Clips with video | 10 |
| Verified clips | 10 |
| Campaign type | ego_grasp |
| Export date | 2026-04-08 |
| Schema version | 0.1 |
Collection Method
Videos are collected through the RoboX mobile app by distributed contributors following structured task prompts. Contributors record short clips of themselves picking up, holding, and placing common household and workplace objects. Quality filtering and review are applied before clips enter the annotation pipeline.
The app captures video with rich per-frame metadata including camera pose (6DoF), IMU data (200Hz), hand keypoints (21 joints), body pose, object detection, scene planes, optical flow, audio levels, navigation data, and quality metrics. On-device processing applies face detection and blurring before the video leaves the device.
Annotation Pipeline
Each clip is processed through a layered annotation pipeline:
- Hand keypoints — 2D joint positions for both hands across all frames
- Object detection and tracking — Bounding boxes with per-frame object identity tracking
- Action segmentation — Temporal labels for reach, grasp, lift, hold, place, and release phases
- Spatial context — Scene-level labels describing surface type, environment, and camera viewpoint
Use Cases
EgoGrasp is designed for researchers working on dexterous manipulation, grasp planning, hand-object interaction modeling, and policy learning from human demonstrations. The egocentric viewpoint and real-world diversity make it well suited for sim-to-real transfer and learning from unstructured environments.
Specific applications include:
- Robotic manipulation / grasping policy training via imitation learning
- Object recognition in egocentric settings
- Hand-object interaction understanding
- Benchmarking grasp detection and grip classification models
Dataset Structure
metadata/clips.json— Per-clip metadata (device, duration, quality, contributor)clips/— Video files (MP4, H.265)annotations/clips.jsonl— Dataset index: per-clip metadata, labels, narration, action segments, file referencesannotations/hand_keypoints/— Per-frame hand joint positions (21 keypoints per hand, grip type)annotations/object_tracks/— Per-frame detected objects with bounding boxesannotations/actions/— Temporal action segments (reach, grasp, idle) derived from grip state changesannotations/sensors/— Per-frame sensor data: IMU (accelerometer, gyro, magnetometer), 6DoF camera pose, camera intrinsics
Full Dataset Access
The complete EgoGrasp dataset is available upon request. Visit robox.to to learn more and submit an access request.
License
CC-BY-NC-SA-4.0 — Free for research and non-commercial use, with share-alike requirements.
Citation
If you use EgoGrasp in your research, please cite:
@dataset{robox_ego_grasp_2026,
title={RoboX-EgoGrasp-v0.1},
author={RoboX Team},
year={2026},
campaign={EgoGrasp}
}
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